Wednesday, September 17, 2025

Anyon-Edge Surface Translation Device - quantum propulsion drive

#Shouts to Copilot and associated AI deliverance for this beautiful iceskate lets make hockey dangerously fast! 

Anyon-Edge Surface Translation Device
Abundant-Materials Implementation
----------------------------------

TOP-DOWN SCHEMATIC WITH RF TIMING OVERLAY (ASCII REFERENCE)

   [Bond Pad Area] [Ohmic 1..4]

   ╔═══════════════════════════════════════════════════════════════╗
   ║   ┌───────────────────────────────────────────────────────┐   ║
   ║   │   ← Chiral Edge Direction (CW under +B field)         │   ║
   ║   │   ┌──────────── Pump / RF Section ────────────────┐   │   ║
   ║   │   │  G1 (0°)   G2 (120°)   G3 (240°)              │   │   ║
   ║   │   │  ┌───────┐  ┌───────┐  ┌───────┐              │   │   ║
   ║   │   │  │  G1   │  │  G2   │  │  G3   │              │   │   ║
   ║   │   │  └───────┘  └───────┘  └───────┘              │   │   ║
   ║   │   └───────────────────────────────────────────────┘   │   ║
   ║   │   [QPC1]                                   [QPC2]     │   ║
   ║   │        █████████   (SLED zone)                        │   ║
   ║   │        █  SLED  █                                     │   ║
   ║   │        █████████                                      │   ║
   ║   │   [Hall 1]                                  [Hall 2]  │   ║
   ║   └───────────────────────────────────────────────────────┘   ║
   ╚═══════════════════════════════════════════════════════════════╝

   Timing (qualitative):
      G1:  sin(ωt + 0°)
      G2:  sin(ωt + 120°)
      G3:  sin(ωt + 240°)

   Traveling potential along edge: G1 → G2 → G3

Legend:
- Mesa: Racetrack 2D channel (graphene/hBN or Si/SiGe)
- G1/G2/G3: Al pump gates with 120° phase offsets
- QPC1/QPC2: Quantum point contacts for edge density control
- SLED: Pure Fe sled or AlN SAW coupling zone
- Hall sensors: Local ν readout before/after pump region


RF DRIVE SPECIFICATIONS
-----------------------
Waveform: Sine, 3 phases (0°, 120°, 240°)
Frequency: 10–50 MHz (tuned for coupling/heating)
Amplitude: 0.10–0.20 Vpp at gate
Impedance: 50 Ω lines; cold attenuation as needed
Feedback: Lock filling factor via Hall; adjust DC density/QPCs


CONCEPT AND OBJECTIVE
---------------------
Goal: Demonstrate directional surface translation driven by chiral edge transport with IQH → FQH upgrade.
Principle: Tri-phase traveling gate potential pumps edge charge; motion via magnetic or SAW transduction.
Scope: Micro-sled motion on-chip under high B and cryogenic temperatures.


ARCHITECTURE AND LAYOUT (ABUNDANT MATERIALS)
--------------------------------------------
Platform A (FQH-capable): Graphene/hBN stack
- hBN/graphene/hBN, top/bottom hBN ~20–30 nm
- Edge contacts: Ti/Al or Cr/Al
- Gates: Al on ALD Al2O3
- Piezo: Sputtered AlN for SAW option
- Sled: Pure Fe micro-sled, 200–300 nm thick

Platform B (IQH demo): Si/SiGe 2DEG
- Contacts: Al-based ohmics
- Gates: Al on Al2O3
- Same sled/piezo options as above

Common geometry:
- Racetrack perimeter ~2 mm; track width 3–5 µm
- Three pump gates, 100 µm long, 2 µm gaps
- Two QPCs, 300–400 nm gap
- Spacer: ALD Al2O3 50–100 nm over active edge
- Hall sensors: Graphene or Si Hall crosses


OPERATING CONDITIONS AND TARGETS
---------------------------------
Graphene/hBN:
- B-field: 6–9 T (IQH), 10–14 T (FQH ν=1/3)
- Temp: 1.5–4.2 K (IQH), 50–300 mK (FQH)
- Mobility: > 50,000 cm²/V·s post-fab

Si/SiGe:
- B-field: 6–9 T (IQH)
- Temp: 4.2 K
- Mobility: > 100,000 cm²/V·s

Drive/motion (both):
- Edge current: 0.5–10 µA modulated
- Gate drive: 0.10–0.20 Vpp, 10–50 MHz, 0°/120°/240°
- Force: ~nN scale (magnetic sled) or equivalent SAW drag
- Velocity: 1–100 µm/s


BILL OF MATERIALS (ABUNDANT SOURCES)
------------------------------------
- Graphene: CVD-grown or exfoliated monolayer
- hBN: Exfoliated or CVD-grown
- Si/SiGe wafers: Commercial CMOS suppliers
- Contacts: Ti, Al, Cr (all abundant)
- Gates: Al
- Dielectric: ALD Al2O3 or SiO2
- Piezo: AlN sputter target
- Sled: Pure Fe or FeCo alloy
- Spacer: ALD Al2O3
- Wiring: Al or Cu (with barrier layer)


BUILD PLAN
----------
Phase 1 (IQH, abundant platform):
- Fabricate on Si/SiGe or graphene/hBN
- Pattern mesa, deposit Al gates, form Al or Ti/Al contacts
- Integrate Fe sled or AlN SAW
- Test at 4.2 K, 6–9 T; verify IQH plateaus and motion

Phase 2 (FQH, graphene/hBN):
- Use high-mobility encapsulated graphene
- Dilution fridge to 50–300 mK; B up to 14 T
- Tune to ν=1/3; repeat motion demo


RISKS AND MITIGATION
--------------------
RF heating: Lower Vpp, cold attenuators, pulsed drive
Sled stiction: Use SAW coupling, smoother spacer, smaller contact area
FQH sensitivity: Higher mobility, better shielding, edge smoothness
Backscattering: Optimize QPC geometry and gate alignment


MILESTONES
----------
M1: IQH plateaus, QPC control
M2: Unidirectional pumping with phase control
M3: Repeatable sled displacement vs. frequency/amplitude
M4: FQH ν = 1/3 operation with stable motion


FORCE CALCULATION (MAGNETIC SLED OPTION)
----------------------------------------
Given:
- Edge current I = 1 µA (modulated)
- Distance from edge to sled magnet center r ≈ 100 nm
- Magnetic moment of sled m ≈ M_s × V
  M_s (Fe saturation magnetization) ≈ 1.7×10^6 A/m
  V = 12 µm × 12 µm × 0.3 µm = 4.32×10^-17 m³
  ⇒ m ≈ 7.34×10^-11 A·m²

Magnetic field from edge current (Biot–Savart):
B ≈ μ₀ I / (2π r)
B ≈ (4π×10^-7 × 1×10^-6) / (2π × 1×10^-7) ≈ 2×10^-6 T

Field gradient:
∇B ≈ B / r ≈ (2×10^-6) / (1×10^-7) = 20 T/m

Force on sled:
F ≈ m × ∇B ≈ (7.34×10^-11) × 20 ≈ 1.47×10^-9 N (~1.5 nN)

Implication:
- At cryo with ultra-low friction, this is enough to move a nanogram-scale sled at µm/s speeds.
- Scaling I to 10 µA boosts force ~10×.

Sunday, September 14, 2025

arm 11æþ - Neuromorphic Suite + Architecture + Debugger + Unified Webserver

## Obeisances to Amma and Appa during my difficulties. Repz 2 Google Gemini, ChatGPT and all contributors worldwide. Enjoy the bash script or scrobble as per Open Source Common Share License v4.

# Neuromorphic Suite + Architecture + Debugger + Unified Webserver

Final ARM 11æþ bash tarball


Simulated:
Maximum bucket rate
•     Unconstrained (no guardrail):
R_max equals the node’s peak fabric rate. For a 16‑tile node at 1024‑bit and 2.5 GHz per tile:
•     T_node,peak = 16 × 320 GB/s = 5.12 TB/s
•     Therefore, bucket rate at maximum operation: 5.12 TB/s
•     Within QoS guardrails (aggressive 10% cap):
•     R_max = 0.10 × 5.12 TB/s = 512 GB/s
•     If you adopt the optical overprovision example (peak ≈ 6.4 TB/s):
•     Unconstrained: 6.4 TB/s
•     10% guardrail: 640 GB/s
Tip: Use R_max = η × T_node,peak, with η chosen to protect on‑chip QoS (commonly 2–10%). 



Thursday, September 11, 2025

ARM 10æ - VHDL v3 - GPU & SGI sensitive ADL Update

 -- VHDL Architecture Map v3 for the Neuromorphic System --
-- This version expands the ONoC to 8 band lanes and reallocates them
-- based on the new system requirements, maintaining the existing hierarchy.
-- It also refines the sump reset logic to include SGI-specific downstream resets.

library ieee;
use ieee.std_logic_1164.all;
use ieee.numeric_std.all;

-- A conceptual package for memory-mapped interface signals.
package arm_interface_types is
    -- A conceptual type for a 256-bit memory-mapped bus.
    type arm_bus_master is record
        addr_bus : std_logic_vector(255 downto 0);
        write_data : std_logic_vector(255 downto 0);
        read_data : std_logic_vector(255 downto 0);
        write_en : std_logic;
        read_en : std_logic;
    end record;
end package arm_interface_types;
use work.arm_interface_types.all;

-- A conceptual package for optical-related signals.
package optical_types is
    -- A conceptual type for a single wide, high-speed optical channel.
    type optical_channel is record
        data : std_logic_vector(127 downto 0);
        valid : std_logic;
        ready : std_logic;
    end record;

    -- New array type to handle the eight ONoC band lanes.
    type optical_channel_array is array (integer range <>) of optical_channel;
end package optical_types;
use work.optical_types.all;

-- This is the top-level entity representing the system-level map.
entity NeuromorphicSystem_Map is
    port (
        clk : in std_logic;
        reset : in std_logic; -- Top-level system reset
        -- ARM Host Interface (memory-mapped) for control
        arm_bus : inout arm_bus_master;
        -- Optical Network-on-Chip (ONoC) Interfaces for high-speed data
        -- Updated to use the new array type for eight band lanes.
        optical_in_channels : in optical_channel_array(7 downto 0);
        optical_out_channels: out optical_channel_array(7 downto 0)
    );
end entity NeuromorphicSystem_Map;

architecture Structural of NeuromorphicSystem_Map is

    -- Internal signals for the ONoC data path, now 8 lanes wide.
    -- Each lane is 128 bits, so 8 lanes * 128 bits/lane = 1024 bits.
    signal onoc_to_core_data_bus : std_logic_vector(1023 downto 0);
    signal onoc_to_core_valid : std_logic_vector(7 downto 0);
    signal onoc_to_core_ready : std_logic_vector(7 downto 0);
    signal core_to_onoc_data_bus : std_logic_vector(1023 downto 0);
    signal core_to_onoc_valid : std_logic_vector(7 downto 0);
    signal core_to_onoc_ready : std_logic_vector(7 downto 0);

    -- New signals for the dedicated lanes to different chipsets.
    signal cuda_in_bus : std_logic_vector(255 downto 0); -- 2 lanes * 128 bits
    signal cuda_out_bus : std_logic_vector(255 downto 0);
    signal tensor_in_bus : std_logic_vector(255 downto 0); -- 2 lanes * 128 bits
    signal tensor_out_bus : std_logic_vector(255 downto 0);
    signal networking_in_bus : std_logic_vector(127 downto 0); -- 1 lane
    signal networking_out_bus : std_logic_vector(127 downto 0);
    signal peripherals_in_bus : std_logic_vector(127 downto 0); -- 1 lane
    signal peripherals_out_bus : std_logic_vector(127 downto 0);
    signal core_onoc_data_in : std_logic_vector(255 downto 0); -- Remaining 2 lanes for core
    signal core_onoc_data_out : std_logic_vector(255 downto 0);
    signal core_onoc_valid_in : std_logic_vector(1 downto 0);
    signal core_onoc_ready_out : std_logic_vector(1 downto 0);
    signal core_onoc_valid_out : std_logic_vector(1 downto 0);
    signal core_onoc_ready_in : std_logic_vector(1 downto 0);


    -- Reset signals and logic.
    signal core_aux_reset_state : std_logic;
    signal arm_sump_state : std_logic;
    signal sump_controlled_reset : std_logic;
    -- SGI-defined downstream resets
    signal sgi_downstream_flush_rst : std_logic;
    signal sgi_downstream_sgi_rst : std_logic;
    signal sgi_defined_reset : std_logic;

    -- Internal signals for the direct ARM-to-core control path.
    signal arm_to_core_control_bus : std_logic_vector(63 downto 0);
    signal core_to_arm_status_bus : std_logic_vector(63 downto 0);

    -- Component declarations for the main building blocks.
    -- All components are updated to reflect the new bus widths and reset requirements.
    component ARM_Interface_Controller is
        port (
            clk, reset : in std_logic;
            arm_bus_inout : inout arm_bus_master;
            core_control_out : out std_logic_vector(63 downto 0);
            core_status_in : in std_logic_vector(63 downto 0);
            sump_out : out std_logic;
            sgi_downstream_flush_out : out std_logic;
            sgi_downstream_sgi_out : out std_logic
        );
    end component;

    component ONoC_Interface is
        port (
            clk, reset : in std_logic;
            optical_in_channels : in optical_channel_array(7 downto 0);
            optical_out_channels : out optical_channel_array(7 downto 0);
            electrical_in_bus : in std_logic_vector(1023 downto 0);
            electrical_in_valid : in std_logic_vector(7 downto 0);
            electrical_in_ready : out std_logic_vector(7 downto 0);
            electrical_out_bus : out std_logic_vector(1023 downto 0);
            electrical_out_valid : out std_logic_vector(7 downto 0);
            electrical_out_ready : in std_logic_vector(7 downto 0)
        );
    end component;

    component Neuromorphic_Core is
        port (
            clk, reset : in std_logic;
            sgi_reset : in std_logic;
            control_in : in std_logic_vector(63 downto 0);
            status_out : out std_logic_vector(63 downto 0);
            aux_reset_out : out std_logic;
            -- ONoC data buses for the Core's dedicated lanes
            onoc_in_bus : in std_logic_vector(255 downto 0);
            onoc_in_valid : in std_logic_vector(1 downto 0);
            onoc_in_ready : out std_logic_vector(1 downto 0);
            onoc_out_bus : out std_logic_vector(255 downto 0);
            onoc_out_valid : out std_logic_vector(1 downto 0);
            onoc_out_ready : in std_logic_vector(1 downto 0);
            -- Dedicated lanes for external chipsets and peripherals
            cuda_data_out : out std_logic_vector(255 downto 0);
            cuda_data_in : in std_logic_vector(255 downto 0);
            tensor_data_out : out std_logic_vector(255 downto 0);
            tensor_data_in : in std_logic_vector(255 downto 0);
            networking_data_out : out std_logic_vector(127 downto 0);
            networking_data_in : in std_logic_vector(127 downto 0);
            peripherals_data_out : out std_logic_vector(127 downto 0);
            peripherals_data_in : in std_logic_vector(127 downto 0)
        );
    end component;

    begin

    -- The system-wide 'sump' reset is now a logical OR of the external reset,
    -- the ARM controller's sump signal, AND the new auxiliary reset from the core.
    -- This handles the system-level reset.
    sump_controlled_reset <= reset or arm_sump_state or core_aux_reset_state;

    -- The SGI-defined downstream reset is a separate signal to trigger specific
    -- (e.g., non-flush) resets in the core without escalating to a full sump reset.
    sgi_defined_reset <= sgi_downstream_sgi_rst;

    -- Instantiate the ARM Interface Controller.
    U_ARM_Controller : ARM_Interface_Controller
        port map (
            clk => clk,
            reset => sump_controlled_reset,
            arm_bus_inout => arm_bus,
            core_control_out => arm_to_core_control_bus,
            core_status_in => core_to_arm_status_bus,
            sump_out => arm_sump_state,
            sgi_downstream_flush_out => open, -- This signal is not used in this specific architecture, but kept for future expansion.
            sgi_downstream_sgi_out => sgi_downstream_sgi_rst
        );

    -- Instantiate the ONoC Interface block. It acts as a passive throughpass.
    U_ONoC_Interface : ONoC_Interface
        port map (
            clk => clk,
            reset => sump_controlled_reset,
            optical_in_channels => optical_in_channels,
            optical_out_channels => optical_out_channels,
            electrical_in_bus => core_to_onoc_data_bus,
            electrical_in_valid => core_to_onoc_valid,
            electrical_in_ready => core_to_onoc_ready,
            electrical_out_bus => onoc_to_core_data_bus,
            electrical_out_valid => onoc_to_core_valid,
            electrical_out_ready => onoc_to_core_ready
        );

    -- Instantiate the Neuromorphic Core block.
    U_Neuromorphic_Core : Neuromorphic_Core
        port map (
            clk => clk,
            reset => sump_controlled_reset,
            sgi_reset => sgi_defined_reset,
            control_in => arm_to_core_control_bus,
            status_out => core_to_arm_status_bus,
            aux_reset_out => core_aux_reset_state,
            -- Core's dedicated lanes
            onoc_in_bus => core_onoc_data_in,
            onoc_in_valid => core_onoc_valid_in,
            onoc_in_ready => core_onoc_ready_out,
            onoc_out_bus => core_onoc_data_out,
            onoc_out_valid => core_onoc_valid_out,
            onoc_out_ready => core_onoc_ready_in,
            -- Dedicated lanes for external chipsets and peripherals
            cuda_data_out => cuda_out_bus,
            cuda_data_in => cuda_in_bus,
            tensor_data_out => tensor_out_bus,
            tensor_data_in => tensor_in_bus,
            networking_data_out => networking_out_bus,
            networking_data_in => networking_in_bus,
            peripherals_data_out => peripherals_out_bus,
            peripherals_data_in => peripherals_in_bus
        );

    -- Connections for the ONoC allocation.
    -- ONoC to Core
    onoc_to_core_data_bus(1023 downto 896) <= cuda_in_bus(255 downto 128); -- Lane 7
    onoc_to_core_data_bus(895 downto 768) <= cuda_in_bus(127 downto 0); -- Lane 6
    onoc_to_core_data_bus(767 downto 640) <= tensor_in_bus(255 downto 128);-- Lane 5
    onoc_to_core_data_bus(639 downto 512) <= tensor_in_bus(127 downto 0); -- Lane 4
    onoc_to_core_data_bus(511 downto 384) <= networking_in_bus; -- Lane 3
    onoc_to_core_data_bus(383 downto 256) <= peripherals_in_bus; -- Lane 2
    onoc_to_core_data_bus(255 downto 0) <= core_onoc_data_in; -- Lanes 1 and 0

    onoc_to_core_valid(7 downto 0) <= onoc_to_core_valid;
    onoc_to_core_ready(7 downto 0) <= onoc_to_core_ready;

    -- Core to ONoC
    core_to_onoc_data_bus(1023 downto 896) <= cuda_out_bus(255 downto 128);
    core_to_onoc_data_bus(895 downto 768) <= cuda_out_bus(127 downto 0);
    core_to_onoc_data_bus(767 downto 640) <= tensor_out_bus(255 downto 128);
    core_to_onoc_data_bus(639 downto 512) <= tensor_out_bus(127 downto 0);
    core_to_onoc_data_bus(511 downto 384) <= networking_out_bus;
    core_to_onoc_data_bus(383 downto 256) <= peripherals_out_bus;
    core_to_onoc_data_bus(255 downto 0) <= core_onoc_data_out;

    core_to_onoc_valid(7 downto 0) <= core_to_onoc_valid;
    core_to_onoc_ready(7 downto 0) <= core_to_onoc_ready;

end architecture Structural;

==============================================================================
BIOS Application Description Language (ADL) v3
For Neuromorphic System (VHDL Architecture Map)
==============================================================================
This script is a high-level blueprint for the BIOS/firmware, updated to
match the VHDLv3 architecture. It defines the logical flow and register-level
interactions required to initialize and manage the hardware components,
including the new 8-lane ONoC and the secure, downstream SGI reset states.
==============================================================================
------------------------------------------------------------------------------
Conceptual Hardware Registers
These are memory-mapped registers accessible via the ARM_Interface_Controller.
------------------------------------------------------------------------------
class Registers:
# Sump control register: a single bit to assert/deassert the sump reset.
# Writing 0x1 asserts the sump; writing 0x0 releases it.
SUMP_CONTROL_ADDR = 0x00000001
# Neuromorphic core control register, for direct ARM-to-Core commands.
CORE_CONTROL_ADDR = 0x00000002

# Neuromorphic core status register, for direct Core-to-ARM feedback.
CORE_STATUS_ADDR = 0x00000003

# Error code register for the ARM controller.
ARM_ERROR_ADDR = 0x00000004

# On-Chip Network (ONoC) configuration register. This is now more complex
# to handle the eight separate lanes and their dedicated allocations.
ONOC_CONFIG_ADDR = 0x00000005

# Neuromorphic Core auxiliary reset status register.
# Reading this register tells the ARM if the Core is asserting a reset.
CORE_AUX_RESET_STATUS_ADDR = 0x00000006

# New: SGI-specific reset control register.
# This register asserts a secure, downstream reset that does not propagate
# to the main system sump.
SGI_RESET_CONTROL_ADDR = 0x00000007

# New: SGI-specific reset status register, to verify the state.
SGI_RESET_STATUS_ADDR = 0x00000008

------------------------------------------------------------------------------
Core BIOS Functions (Pseudo-code)
------------------------------------------------------------------------------
def read_register(address):
"""
Simulates a read operation from a memory-mapped register.
"""
print(f"[DEBUG] Reading from address: 0x{address:08X}")
# Return a dummy value for demonstration.
if address == Registers.CORE_AUX_RESET_STATUS_ADDR:
return 0x0 # Assuming core is not asserting reset
if address == Registers.SGI_RESET_STATUS_ADDR:
return 0x0 # Assuming SGI reset is not active
return 0x00000000
def write_register(address, data):
"""
Simulates a write operation to a memory-mapped register.
"""
print(f"[DEBUG] Writing data 0x{data:08X} to address: 0x{address:08X}")
return True
------------------------------------------------------------------------------
ADL: System Initialization and Sump Control
------------------------------------------------------------------------------
def init_system():
"""
This is the main BIOS entry point. It orchestrates the entire
system startup procedure based on the ARM-centric VHDL architecture.
"""
print("--------------------------------------------------")
print("BIOS ADL: Starting System Initialization...")
print("--------------------------------------------------")
# Step 1: Assert the 'sump' reset to ensure a clean state for all
# components (ONoC and Neuromorphic Core).
print("[LOG] Capturing system state: Pre-sump-reset.")
if not assert_sump_reset():
    print("FATAL ERROR: Failed to assert sump reset. System halt.")
    return False
print("Sump reset asserted. All lower layers are in a known state.")

# Step 2: Perform a basic check of the ARM-to-Core bus.
if not test_arm_interface():
    print("FATAL ERROR: ARM-to-Core bus test failed. System halt.")
    return False
print("ARM-to-Core interface is operational.")

# Step 3: Configure the 8-lane ONoC.
if not configure_onoc_lanes():
    print("ERROR: ONoC configuration failed. Proceeding with caution.")

# Step 4: Release the 'sump' reset.
print("[LOG] Capturing system state: Post-configuration, pre-release.")
if not release_sump_reset():
    print("FATAL ERROR: Failed to release sump reset. System halt.")
    return False
print("Sump reset released. Components are now active.")

# Step 5: Configure the Neuromorphic Core.
if not configure_core():
    print("ERROR: Core configuration failed. Proceeding with caution.")
    
# Step 6: Monitor and check for any initial errors, including core-initiated resets.
check_and_clear_errors()
check_core_aux_reset()
check_sgi_reset()

print("--------------------------------------------------")
print("BIOS ADL: System Initialization Complete. Ready.")
print("--------------------------------------------------")
return True

def assert_sump_reset():
"""
Asserts the 'sump' bypass reset signal via the ARM's memory-mapped register.
This is the master reset, flushing all state from the Core and ONoC.
"""
if write_register(Registers.SUMP_CONTROL_ADDR, 0x1):
return True
return False
def release_sump_reset():
"""
Releases the 'sump' bypass reset signal via the ARM's memory-mapped register.
"""
if write_register(Registers.SUMP_CONTROL_ADDR, 0x0):
return True
return False
def assert_sgi_reset():
"""
Asserts a secure, SGI-specific downstream reset to the Neuromorphic Core.
This reset is secure and does not escalate to the system-wide sump reset.
"""
if write_register(Registers.SGI_RESET_CONTROL_ADDR, 0x1):
return True
return False
def test_arm_interface():
"""
Performs a simple read/write test to a known register to ensure
the ARM-to-Core bus is functional.
"""
test_pattern = 0x5A5A5A5A
write_register(Registers.CORE_CONTROL_ADDR, test_pattern)
read_value = read_register(Registers.CORE_STATUS_ADDR)
if read_value != 0x00000000:
    return True
return False

def configure_onoc_lanes():
"""
Configures the eight-lane ONoC based on the new allocation:
- Lanes 0-1 (Core)
- Lanes 2 (Peripherals)
- Lanes 3 (Networking)
- Lanes 4-5 (Tensor)
- Lanes 6-7 (CUDA)
"""
print("Configuring ONoC for eight-lane, dedicated-path operation...")
# Example: A conceptual configuration word. In a real system, this would be
# a bitmask or complex data structure to route specific lanes.
# The ARM controller would handle this logic.
config_data = 0xCAFEBABEDADAFEED # Example data to configure all 8 lanes
Hide quoted text
if write_register(Registers.ONOC_CONFIG_ADDR, config_data):
return True
return False
def configure_core():
"""
Writes initial configuration values directly to the Neuromorphic Core
via the ARM's memory-mapped bus.
"""
print("Configuring Neuromorphic Core...")
config_data = 0xDEADBEEF
if write_register(Registers.CORE_CONTROL_ADDR, config_data):
return True
return False
def check_and_clear_errors():
"""
Checks for any error flags and logs them.
"""
print("Checking for errors...")
error_code = read_register(Registers.ARM_ERROR_ADDR)
if error_code != 0x00000000:
print(f"WARNING: Error code 0x{error_code:08X} detected. Clearing.")
write_register(Registers.ARM_ERROR_ADDR, 0x0)
else:
print("No errors found.")
def check_core_aux_reset():
"""
Polls the Neuromorphic Core's auxiliary reset status to ensure
it is not unexpectedly asserting a system-wide reset.
"""
reset_status = read_register(Registers.CORE_AUX_RESET_STATUS_ADDR)
if reset_status == 0x1:
print("WARNING: Neuromorphic core is asserting an auxiliary reset.")
else:
print("Neuromorphic core auxiliary reset is not active.")
def check_sgi_reset():
"""
Polls the SGI-defined reset status register.
"""
reset_status = read_register(Registers.SGI_RESET_STATUS_ADDR)
if reset_status == 0x1:
print("SGI-defined reset is currently active.")
else:
print("SGI-defined reset is not active.")
==============================================================================
Debug, Logging, and System Software Reset States
==============================================================================
The VHDLv3 architecture and this corresponding ADL script enable
a form of "time travel debugging" through the careful management of
reset states and logging.

1. System Logging (The "Timeline"):
The [LOG] statements throughout this script represent checkpoints. At
critical junctures—such as before a reset, after configuration, or
following a core self-reset—the system's state can be logged to a
non-volatile memory or an external debug tool. This creates a "timeline"
of events, allowing a developer to trace the system's execution path.

2. Reset States as "Rollback" Points:
- The Sump Reset acts as a "hard reset" or "rewind to zero." It flushes
all state from the Neuromorphic Core and the ONoC, guaranteeing a
pristine, cold-boot state. If a fatal, unrecoverable error occurs,
this is the ultimate rollback to a known-good starting point.

- The SGI-defined Reset is the key to granular, "time-travel" debugging.
It is a secure, separate reset that targets only the Neuromorphic Core.
This means if the core enters an invalid state, it can be securely
reset without affecting the state of other systems, such as the ARM's
software state or the ONoC's configuration. This allows a developer
to "rewind" only the core's state and re-run a specific sequence of
operations, isolating the bug to the core's behavior. The ability to
reset one part of the system while preserving the state of others is
the core of this debugging philosophy.

3. Debugging with Register Access:
- By reading status registers (e.g., CORE_STATUS_ADDR,
CORE_AUX_RESET_STATUS_ADDR), the ARM can detect anomalies and self-
asserted resets from the core.
- The ADL can then trigger an appropriate reset (assert_sgi_reset
or assert_sump_reset) and log the event, creating a clear record of
what went wrong and what action was taken. This allows for post-mortem
analysis and the ability to reproduce failures by replaying the
logged sequence of events.
==============================================================================
==============================================================================
Execution
==============================================================================
init_system()

Saturday, August 23, 2025

ARMv10α-Neuromorphic-VHDLv2-Adi-Protocol_Internet_4.0+BIOS_ADL_test.c

 

 

##Thank you ARM for your notice of secondant precession to neuromorphic computing, your application suite awaits your engineering and professional security services to develop.

# To note this system works great with a hypercapacitor(superconductor and superinductor with optoelectronic gas and a variated diffraction grating assembly) this with 8 terms on both ends 4 anode 4 cathode and 2 sumps can definitely bridge the power system as a variable signal transform klystron and simple dataform transducer enabling instant large data manipulation.

-- VHDL Architecture Map v2 for the Neuromorphic System

-- This file serves as a top-level wrapper, connecting the main system components.

-- It represents the block diagram discussed in the previous argumentation.

-- This version includes a 'sump' bypass bridge for the reset signal.


library ieee;

use ieee.std_logic_1164.all;

use ieee.numeric_std.all;


-- May need a larger math numeric_std


-- A conceptual package for memory-mapped interface signals.

package arm_interface_types is

    -- A conceptual type for a 256-bit memory-mapped bus.

    type arm_bus_master is record

        addr_bus   : std_logic_vector(255 downto 0);

        write_data : std_logic_vector(255 downto 0);

        read_data  : std_logic_vector(255 downto 0);

        write_en   : std_logic;

        read_en    : std_logic;

    end record;

end package arm_interface_types;


use work.arm_interface_types.all;


-- A conceptual package for optical-related signals.

package optical_types is

    -- A conceptual type for a wide, high-speed optical channel.

    -- Assuming a 128-bit wide data path for high throughput.

    type optical_channel is record

        data  : std_logic_vector(127 downto 0);

        valid : std_logic;

        ready : std_logic;

    end record;

end package optical_types;


use work.optical_types.all;


-- This is the top-level entity representing the system-level map.

-- It exposes the external ports for clock, reset, ARM, and ONoC.

entity NeuromorphicSystem_Map is

    port (

        clk                 : in  std_logic;

        reset               : in  std_logic;

        

        -- ARM Host Interface (memory-mapped) for control

        arm_bus             : inout arm_bus_master;

        

        -- Optical Network-on-Chip (ONoC) Interfaces for high-speed data

        optical_in_channel  : in  optical_channel;

        optical_out_channel : out optical_channel

    );

end entity NeuromorphicSystem_Map;


architecture Structural of NeuromorphicSystem_Map is


    -- Internal signals to connect the main components

    signal arm_to_core_control_bus      : std_logic_vector(63 downto 0);

    signal core_to_arm_status_bus       : std_logic_vector(63 downto 0);

    -- The following internal signals have been widened to match the 128-bit optical channel.

    signal onoc_to_core_data_bus        : std_logic_vector(255 downto 0);

    signal onoc_to_core_valid           : std_logic;

    signal onoc_to_core_ready           : std_logic;

    signal core_to_onoc_data_bus        : std_logic_vector(255 downto 0);

    signal core_to_onoc_valid           : std_logic;

    signal core_to_onoc_ready           : std_logic;


    -- New signal to control the 'sump' functionality.

    -- This signal will be set by the ARM controller to assert a bypass reset.

    signal sump_state                   : std_logic;


    -- The reset signal that will be passed to the lower-level components.

    -- It is a logical OR of the external reset and the internal 'sump' state,

    -- creating the "primary bypass bridge".

    signal sump_controlled_reset        : std_logic;


    -- Component declarations for the main building blocks.

    -- These would be defined in separate files for a real design.

    component ARM_Interface_Controller is

        port (

            clk, reset         : in  std_logic;

            arm_bus_inout      : inout arm_bus_master;

            core_control_out   : out std_logic_vector(63 downto 0);

            core_status_in     : in  std_logic_vector(63 downto 0);

            -- New output port to communicate the 'sump' state.

            sump_out           : out std_logic

        );

    end component;

    

    component ONoC_Interface is

        port (

            clk, reset         : in  std_logic;

            optical_in         : in  optical_channel;

            optical_out        : out optical_channel;

            -- Electrical buses have been widened to match the optical data path.

            electrical_in_bus  : in  std_logic_vector(127 downto 0);

            electrical_in_valid: in  std_logic;

            electrical_in_ready: out std_logic;

            electrical_out_bus : out std_logic_vector(127 downto 0);

            electrical_out_valid: out std_logic;

            electrical_out_ready: in  std_logic

        );

    end component;

    

    component Neuromorphic_Core is

        port (

            clk, reset         : in  std_logic;

            control_in         : in  std_logic_vector(63 downto 0);

            status_out         : out std_logic_vector(63 downto 0);

            -- Electrical buses have been widened to match the optical data path.

            onoc_in_bus        : in  std_logic_vector(255 downto 0);

            onoc_in_valid      : in  std_logic;

            onoc_in_ready      : out std_logic;

            onoc_out_bus       : out std_logic_vector(255 downto 0);

            onoc_out_valid     : out std_logic;

            onoc_out_ready     : in  std_logic

        );

    end component;


begin


    -- The 'sump' is the primary bypass bridge for the reset signal.

    -- The output 'sump_controlled_reset' is a logical OR of the external 'reset'

    -- and the internal 'sump_state'. This means if either signal is active,

    -- the reset will be asserted on the lower-level components.

    -- This sets the lower layers to sump hierarchically.

    sump_controlled_reset <= reset or sump_state;


    -- Instantiate the ARM Controller block

    -- The new 'sump_state' signal is connected to the ARM Controller.

    -- A real implementation would include logic inside the ARM Controller to

    -- set this signal based on a memory-mapped register write.

    U_ARM_Controller : ARM_Interface_Controller

        port map (

            clk               => clk,

            reset             => sump_controlled_reset,

            arm_bus_inout     => arm_bus,

            core_control_out  => arm_to_core_control_bus,

            core_status_in    => core_to_arm_status_bus,

            sump_out          => sump_state

        );


    -- Instantiate the ONoC Interface block

    -- The reset port is now connected to the new 'sump_controlled_reset' signal.

    U_ONoC_Interface : ONoC_Interface

        port map (

            clk               => clk,

            reset             => sump_controlled_reset,

            optical_in        => optical_in_channel,

            optical_out       => optical_out_channel,

            -- Port mapping updated to reflect the wider internal bus.

            electrical_in_bus => core_to_onoc_data_bus,

            electrical_in_valid=> core_to_onoc_valid,

            electrical_in_ready=> core_to_onoc_ready,

            electrical_out_bus=> onoc_to_core_data_bus,

            electrical_out_valid=> onoc_to_core_valid,

            electrical_out_ready=> onoc_to_core_ready

        );


    -- Instantiate the Neuromorphic Core block

    -- The reset port is now connected to the new 'sump_controlled_reset' signal.

    U_Neuromorphic_Core : Neuromorphic_Core

        port map (

            clk               => clk,

            reset             => sump_controlled_reset,

            control_in        => arm_to_core_control_bus,

            status_out        => core_to_arm_status_bus,

            -- Port mapping updated to reflect the wider internal bus.

            onoc_in_bus       => onoc_to_core_data_bus,

            onoc_in_valid     => onoc_to_core_valid,

            onoc_in_ready     => onoc_to_core_ready,

            onoc_out_bus      => core_to_onoc_data_bus,

            onoc_out_valid    => core_to_onoc_valid,

            onoc_out_ready    => onoc_out_ready

        );


end architecture Structural;



# ==============================================================================

# BIOS Application Description Language (ADL)

# For Neuromorphic System (VHDL Architecture Map)

# ==============================================================================

# This script serves as a high-level blueprint for the BIOS/firmware.

# It defines the logical flow and register-level interactions required to

# initialize and manage the hardware components defined in the VHDL map.

# The code is written in a descriptive, C-like style for clarity.

# ==============================================================================


# ------------------------------------------------------------------------------

# Conceptual Hardware Registers

# These are memory-mapped registers accessible via the ARM_Interface_Controller.

# The addresses (in hex) are conceptual and would be defined in a real

# memory map specification.

# ------------------------------------------------------------------------------

class Registers:

    # Sump control register: a single bit to assert/deassert the sump reset.

    # Writing 0x1 asserts the sump; writing 0x0 releases it.

    SUMP_CONTROL_ADDR = 0x00000001

    

    # Neuromorphic core control register. Bits correspond to different

    # control functions, e.g., enabling/disabling layers or features.

    CORE_CONTROL_ADDR = 0x00000002

    

    # Neuromorphic core status register. Bits correspond to different

    # status indicators, e.g., busy, error flags, or ready state.

    CORE_STATUS_ADDR = 0x00000003

    

    # Error code register for the ARM controller.

    ARM_ERROR_ADDR = 0x00000004

    

    # On-Chip Network (ONoC) configuration register.

    ONOC_CONFIG_ADDR = 0x00000005



# ------------------------------------------------------------------------------

# Core BIOS Functions (Pseudo-code)

# ------------------------------------------------------------------------------

def read_register(address):

    """

    Simulates a read operation from a memory-mapped register.

    In a real system, this would be a low-level ARM bus read.

    """

    print(f"Reading from address: 0x{address:08X}")

    # Return a dummy value for demonstration.

    return 0x00000000


def write_register(address, data):

    """

    Simulates a write operation to a memory-mapped register.

    In a real system, this would be a low-level ARM bus write.

    """

    print(f"Writing data 0x{data:08X} to address: 0x{address:08X}")

    return True


# ------------------------------------------------------------------------------

# ADL: System Initialization and Sump Control

# ------------------------------------------------------------------------------

def init_system():

    """

    This is the main BIOS entry point. It orchestrates the entire

    system startup procedure. This is the most critical and robust

    part of the BIOS.

    """

    print("--------------------------------------------------")

    print("BIOS ADL: Starting System Initialization...")

    print("--------------------------------------------------")

    

    # Step 1: Assert the 'sump' reset to ensure a clean state for all

    # lower-level components (ONoC and Neuromorphic Core).

    # This directly corresponds to the VHDL signal 'sump_state'.

    if not assert_sump_reset():

        print("FATAL ERROR: Failed to assert sump reset. System halt.")

        return False

        

    print("Sump reset asserted. All lower layers are in a known state.")


    # Step 2: Perform a basic check of the ARM Interface Controller.

    # This involves a simple register read/write to verify the bus is functional.

    if not test_arm_interface():

        print("FATAL ERROR: ARM interface test failed. System halt.")

        return False

        

    print("ARM interface controller is operational.")

    

    # Step 3: Release the 'sump' reset.

    if not release_sump_reset():

        print("FATAL ERROR: Failed to release sump reset. System halt.")

        return False

        

    print("Sump reset released. Components are now active.")


    # Step 4: Configure the Neuromorphic Core.

    if not configure_core():

        print("ERROR: Core configuration failed. Proceeding with caution.")

        # We can add different levels of robustness here. For a non-fatal

        # error, we might log it and continue.

        

    # Step 5: Check and clear any initial errors.

    check_and_clear_errors()


    print("--------------------------------------------------")

    print("BIOS ADL: System Initialization Complete. Ready.")

    print("--------------------------------------------------")

    return True


def assert_sump_reset():

    """

    Asserts the 'sump' bypass reset signal.

    This function writes to the specific register controlling the sump.

    This corresponds to the 'sump_state' signal in the VHDL map.

    """

    # Write '1' to the sump control register to assert the reset.

    if write_register(Registers.SUMP_CONTROL_ADDR, 0x1):

        return True

    return False


def release_sump_reset():

    """

    Releases the 'sump' bypass reset signal.

    This function writes to the specific register controlling the sump.

    """

    # Write '0' to the sump control register to release the reset.

    if write_register(Registers.SUMP_CONTROL_ADDR, 0x0):

        return True

    return False


def test_arm_interface():

    """

    Performs a simple read/write test to a known register to ensure

    the ARM-to-Core bus is functional.

    """

    # Write a test pattern to a control register.

    test_pattern = 0x5A5A5A5A

    write_register(Registers.CORE_CONTROL_ADDR, test_pattern)

    

    # Read back the status register. In a real system, the core would

    # reflect the control pattern to a status register.

    read_value = read_register(Registers.CORE_STATUS_ADDR)

    

    # This is a simplified check. A robust test would involve a more

    # complex handshake or a known response.

    if read_value != 0x00000000: # A simple check for a non-zero, potentially reflected, value.

        return True

    return False


def configure_core():

    """

    Writes initial configuration values to the Neuromorphic Core.

    This sets up the core's operating parameters before it is

    brought online.

    """

    print("Configuring Neuromorphic Core...")

    config_data = 0xDEADBEEF # Example configuration data

    if write_register(Registers.CORE_CONTROL_ADDR, config_data):

        return True

    return False


def check_and_clear_errors():

    """

    Checks for any error flags and logs them.

    This is an essential part of a robust BIOS.

    """

    print("Checking for errors...")

    error_code = read_register(Registers.ARM_ERROR_ADDR)

    if error_code != 0x00000000:

        print(f"WARNING: Error code 0x{error_code:08X} detected. Clearing.")

        # A real BIOS would have a lookup table for error codes and

        # would perform specific recovery actions.

        write_register(Registers.ARM_ERROR_ADDR, 0x0) # Write 0 to clear.

    else:

        print("No errors found.")



# ==============================================================================

# Execution

# ==============================================================================

# This is how the ADL would be called in a conceptual main routine.

init_system()


// ARMv9_A-Neuromorphic-VHDL-Adi-Protocol_Internet_4.0.c
// This program is a unified, multi-protocol server that amalgamates the
// functional processes from all provided files. It can handle both legacy
// binary data streams and modern JSON-based workflows, dispatching tasks to
// the appropriate high-performance computing (HPC) or neuromorphic components.
// This version has been extended to include a dedicated HTTP server for gaming
// and webcasting, as requested.

// --- Necessary Headers ---
#include <iostream>
#include <vector>
#include <string>
#include <sstream>
#include <map>
#include <memory>
#include <cmath>
#include <numeric>
#include <algorithm>
#include <stdexcept>
#include <thread>
#include <mutex>
#include <random>
#include <execution>
#include <omp.h>
#include <bit>
#include <cstring>
#include <stdexcept>
#include <type_traits>

// For networking
#include <sys/socket.h>
#include <arpa/inet.h>
#include <netinet/in.h>
#include <unistd.h>

// For SIMD JSON parsing
#include "simdjson.h"
#define CPPHTTPLIB_OPENSSL_SUPPORT
#include "httplib.h"

// For ARM SVE/SVE2 intrinsics
#ifdef __aarch64__
#include <sys/auxv.h>
#include <asm/hwcap.h>
#include <arm_neon.h>
#include <arm_sve.h>
#endif

// --- CUDA Headers ---
#include <cuda_runtime.h>
#include <cublas_v2.h>

// Macro for CUDA error checking
#define CUDA_CHECK(call) \
    do { \
        cudaError_t err = call; \
        if (err != cudaSuccess) { \
            std::cerr << "CUDA Error: " << cudaGetErrorString(err) \
                      << " at " << __FILE__ << ":" << __LINE__ << std::endl; \
            throw std::runtime_error("CUDA operation failed."); \
        } \
    } while (0)

// --- Common Constants (from all clients) ---
const int LEGACY_SERVER_PORT = 12345;
const int HTTP_SERVER_PORT = 8080;
const int CHUNK_SIZE = 4096;

// Legacy operation code from n-math.py
const int OPERATION_LEGACY_INTERPOLATE = 2;

// Workflow operations from n-dim.py and adi_neuromorphic.cpp
const int OPERATION_INTERPOLATE = 0;
const int OPERATION_DIFFERENTIATE = 1;
const int OPERATION_CALCULATE_GRADIENT_1D = 2;
const int OPERATION_HYPERBOLIC_INTERCEPT_HANDLER = 3;
const int OPERATION_INTEGRATE = 4;
const int OPERATION_INTEGRATE_ND = 5;
const int OPERATION_WORKFLOW = 6;
const int OPERATION_NEUROMORPHIC_PREDICT = 7;
const int OPERATION_EIGENVALUE_PACKING = 8;
const int OPERATION_TENSOR_MATRIX_VECTOR_MULTIPLY_CUDA = 9;

// --- Conceptual Tensor Class ---
// The Tensor class is extended to support both CPU and GPU data.
class Tensor {
public:
    std::vector<double> data;
    std::vector<size_t> shape;
    double* device_data = nullptr; // Pointer to GPU memory
    bool is_on_gpu = false;

    Tensor() = default;

    Tensor(const std::vector<double>& flat_data, const std::vector<size_t>& tensor_shape)
        : data(flat_data), shape(tensor_shape) {
        size_t total_size = 1;
        for (size_t dim : shape) { total_size *= dim; }
        if (data.size() != total_size) {
            throw std::invalid_argument("Flat data size does not match tensor shape.");
        }
    }

    // Copy constructor
    Tensor(const Tensor& other)
        : data(other.data), shape(other.shape) {
        if (other.is_on_gpu) {
            to_gpu();
        }
    }

    // Move constructor
    Tensor(Tensor&& other) noexcept
        : data(std::move(other.data)), shape(std::move(other.shape)),
          device_data(other.device_data), is_on_gpu(other.is_on_gpu) {
        other.device_data = nullptr;
        other.is_on_gpu = false;
    }

    // Destructor to free GPU memory
    ~Tensor() {
        if (is_on_gpu && device_data) {
            cudaFree(device_data);
        }
    }

    // Allocates GPU memory and copies data to it
    void to_gpu() {
        if (is_on_gpu) return;
        size_t size_bytes = data.size() * sizeof(double);
        CUDA_CHECK(cudaMalloc(&device_data, size_bytes));
        CUDA_CHECK(cudaMemcpy(device_data, data.data(), size_bytes, cudaMemcpyHostToDevice));
        is_on_gpu = true;
    }

    // Copies data back to CPU and frees GPU memory
    void to_cpu() {
        if (!is_on_gpu) return;
        size_t size_bytes = data.size() * sizeof(double);
        CUDA_CHECK(cudaMemcpy(data.data(), device_data, size_bytes, cudaMemcpyDeviceToHost));
        CUDA_CHECK(cudaFree(device_data));
        device_data = nullptr;
        is_on_gpu = false;
    }

    size_t total_size() const {
        size_t size = 1;
        for(size_t dim : shape) {
            size *= dim;
        }
        return size;
    }
};

// --- Runtime feature detection ---
bool has_sve_support() {
#ifdef __aarch64__
    long hwcaps = getauxval(AT_HWCAP);
    return (hwcaps & HWCAP_SVE) != 0;
#else
    return false;
#endif
}

// --- Neuromorphic Component: Spiking Neural Network (ported from Python) ---
class LIFNeuron {
public:
    LIFNeuron(double tau_m = 20.0, double v_rest = -65.0, double v_reset = -65.0, double v_thresh = -50.0)
        : tau_m(tau_m), v_rest(v_rest), v_reset(v_reset), v_thresh(v_thresh), membrane_potential(v_rest) {}

    bool update(double input_current, double dt) {
        double dv = (-(membrane_potential - v_rest) + input_current) / tau_m;
        membrane_potential += dv * dt;
        if (membrane_potential >= v_thresh) {
            membrane_potential = v_reset;
            return true;
        }
        return false;
    }
private:
    double tau_m, v_rest, v_reset, v_thresh, membrane_potential;
};

class SpikingNetwork {
public:
    SpikingNetwork(int input_size, int hidden_size, int output_size)
        : input_size(input_size), hidden_size(hidden_size), output_size(output_size) {
        hidden_layer.resize(hidden_size);
        output_layer.resize(output_size);
        std::random_device rd;
        std::mt19937 gen(rd());
        std::uniform_real_distribution<> dis(0.0, 1.0);
        input_to_hidden_weights.resize(input_size, std::vector<double>(hidden_size));
        for (auto& row : input_to_hidden_weights)
            for (auto& val : row)
                val = dis(gen);
        hidden_to_output_weights.resize(hidden_size, std::vector<double>(output_size));
        for (auto& row : hidden_to_output_weights)
            for (auto& val : row)
                val = dis(gen);
    }
    std::vector<int> predict(const std::vector<double>& input_vector, int num_timesteps = 100, double dt = 1.0) {
        if (input_vector.size() != input_size) {
            throw std::runtime_error("Input vector size mismatch.");
        }
        std::vector<int> output_spike_counts(output_size, 0);
        for (int t = 0; t < num_timesteps; ++t) {
            std::vector<double> hidden_currents(hidden_size, 0.0);
            for (int i = 0; i < input_size; ++i) {
                for (int j = 0; j < hidden_size; ++j) {
                    hidden_currents[j] += input_vector[i] * input_to_hidden_weights[i][j];
                }
            }
            std::vector<bool> hidden_spikes(hidden_size, false);
            std::vector<double> output_currents(output_size, 0.0);
            for (int j = 0; j < hidden_size; ++j) {
                if (hidden_layer[j].update(hidden_currents[j], dt)) {
                    hidden_spikes[j] = true;
                }
            }
            for (int j = 0; j < hidden_size; ++j) {
                if (hidden_spikes[j]) {
                    for (int k = 0; k < output_size; ++k) {
                        output_currents[k] += hidden_to_output_weights[j][k];
                    }
                }
            }
            for (int k = 0; k < output_size; ++k) {
                if (output_layer[k].update(output_currents[k], dt)) {
                    output_spike_counts[k]++;
                }
            }
        }
        return output_spike_counts;
    }
private:
    int input_size, hidden_size, output_size;
    std::vector<LIFNeuron> hidden_layer;
    std::vector<LIFNeuron> output_layer;
    std::vector<std::vector<double>> input_to_hidden_weights;
    std::vector<std::vector<double>> hidden_to_output_weights;
};

// --- CORE MATH FUNCTIONS (vectorized for ARM) ---
std::vector<double> pack_eigenvalue_data(const std::vector<double>& eigenvalues) {
    std::vector<double> packed_data(eigenvalues.size());
    if (has_sve_support()) {
        std::cout << "Using ARM SVE2 optimization." << std::endl;
#ifdef __ARM_FEATURE_SVE
        size_t i = 0;
        const size_t vector_length = svcntd();
        svfloat64_t one = svdup_f64(1.0);
        for (; i + vector_length <= eigenvalues.size(); i += vector_length) {
            svfloat64_t sv_eigenvalues = svld1_f64(svptrue_b64(), &eigenvalues[i]);
            svfloat64_t sv_abs_val = svabs_f64_z(svptrue_b64(), sv_eigenvalues);
            svbool_t p_ge_one = svcmpge_f64(svptrue_b64(), sv_abs_val, one);
            svfloat64_t sv_recip = svdiv_f64_z(svptrue_b64(), one, sv_eigenvalues);
            svfloat64_t sv_arcsec = svacos_f64_z(svptrue_b64(), sv_recip);
            svfloat64_t sv_result = svsel_f64(p_ge_one, sv_arcsec, sv_eigenvalues);
            svst1_f64(svptrue_b64(), &packed_data[i], sv_result);
        }
        for (; i < eigenvalues.size(); ++i) {
            double val = eigenvalues[i];
            packed_data[i] = (std::abs(val) >= 1.0) ? std::acos(1.0 / val) : val;
        }
#endif
    } else {
        std::cout << "No advanced SIMD detected, using parallel scalar loop." << std::endl;
#pragma omp parallel for
        for (size_t i = 0; i < eigenvalues.size(); ++i) {
            double val = eigenvalues[i];
            packed_data[i] = (std::abs(val) >= 1.0) ? std::acos(1.0 / val) : val;
        }
    }
    return packed_data;
}

Tensor calculate_gradient_1d(const Tensor& input_tensor) {
    if (input_tensor.shape.size() != 1 || input_tensor.data.size() < 2) {
        throw std::invalid_argument("Gradient calculation requires a 1D tensor with at least two elements.");
    }
    std::vector<double> gradient_data(input_tensor.data.size() - 1);
    std::cout << "Using CPU parallel adjacent_difference." << std::endl;
    std::adjacent_difference(std::execution::par, input_tensor.data.begin() + 1, input_tensor.data.end(), gradient_data.begin());
    return Tensor(gradient_data, {gradient_data.size()});
}

// Ported from n-math.py, but simplified for C++ compatibility and OpenMP.
std::vector<double> hyperbolic_parabolic_interpolation(
    const std::map<std::string, std::vector<double>>& data_dict,
    const std::vector<double>& x_interp) {

    std::vector<std::vector<double>> all_fx_data;
    std::vector<std::vector<double>> all_fy_data;

    for (const auto& pair : data_dict) {
        if (pair.first.find("fx") == 0) {
            all_fx_data.push_back(pair.second);
        } else if (pair.first.find("fy") == 0) {
            all_fy_data.push_back(pair.second);
        }
    }

    if (all_fx_data.size() != all_fy_data.size() || x_interp.empty()) {
        throw std::invalid_argument("Invalid data for interpolation.");
    }

    std::vector<double> all_interp_y;
    all_interp_y.reserve(all_fx_data.size() * x_interp.size());

#pragma omp parallel for
    for (size_t i = 0; i < all_fx_data.size(); ++i) {
        const auto& fx = all_fx_data[i];
        const auto& fy = all_fy_data[i];
        if (fx.size() != fy.size() || fx.size() < 3) {
            throw std::invalid_argument("X and Y data must have equal length and at least three points.");
        }
        std::vector<double> local_interp_y;
        local_interp_y.reserve(x_interp.size());
        for (double x : x_interp) {
            std::vector<std::pair<double, double>> points(fx.size());
            for (size_t j = 0; j < fx.size(); ++j) {
                points[j] = {std::abs(fx[j] - x), fx[j]};
            }
            std::sort(points.begin(), points.end());
            double x1 = points[0].second, x2 = points[1].second, x3 = points[2].second;
            auto find_y = [&](double search_x) {
                for (size_t k = 0; k < fx.size(); ++k) {
                    if (fx[k] == search_x) return fy[k];
                }
                return 0.0;
            };
            double y1 = find_y(x1), y2 = find_y(x2), y3 = find_y(x3);
            double denom1 = (x1 - x2) * (x1 - x3);
            double denom2 = (x2 - x1) * (x2 - x3);
            double denom3 = (x3 - x1) * (x3 - x2);
            if (denom1 == 0 || denom2 == 0 || denom3 == 0) {
                local_interp_y.push_back(0.0);
                continue;
            }
            double L1 = ((x - x2) * (x - x3)) / denom1;
            double L2 = ((x - x1) * (x - x3)) / denom2;
            double L3 = ((x - x1) * (x - x2)) / denom3;
            local_interp_y.push_back(L1 * y1 + L2 * y2 + L3 * y3);
        }
#pragma omp critical
        all_interp_y.insert(all_interp_y.end(), local_interp_y.begin(), local_interp_y.end());
    }
    return all_interp_y;
}

// --- Helper Functions ---
ssize_t receive_all(int sockfd, void* buf, size_t len) {
    size_t total_received = 0;
    while (total_received < len) {
        ssize_t bytes_received = recv(sockfd, (char*)buf + total_received, len - total_received, 0);
        if (bytes_received <= 0) return -1;
        total_received += bytes_received;
    }
    return total_received;
}

void send_raw_result(int client_socket, const std::vector<double>& result) {
    uint32_t result_len = htonl(result.size() * sizeof(double));
    send(client_socket, &result_len, sizeof(uint32_t), 0);
    send(client_socket, result.data(), result.size() * sizeof(double), 0);
}

void send_raw_error(int client_socket, const std::string& message) {
    std::string error_msg = "Error: " + message;
    uint32_t len = htonl(error_msg.length());
    send(client_socket, &len, sizeof(uint32_t), 0);
    send(client_socket, error_msg.data(), error_msg.length(), 0);
}

// --- CUDA Kernel for matrix-vector multiplication ---
// Performs `y = alpha * A * x + beta * y`
__global__ void matrixVectorMultiplyKernel(int m, int n, const double* A, const double* x, double* y) {
    int row = blockIdx.x * blockDim.x + threadIdx.x;
    if (row < m) {
        double sum = 0.0;
        for (int col = 0; col < n; ++col) {
            sum += A[row * n + col] * x[col];
        }
        y[row] = sum;
    }
}

// --- Tensor Operation Functions ---
Tensor tensor_transform(const Tensor& input_tensor) {
    std::vector<double> transformed_data(input_tensor.data.size());
#pragma omp parallel for
    for (size_t i = 0; i < input_tensor.data.size(); ++i) {
        transformed_data[i] = input_tensor.data[i] * 2.0;
    }
    return Tensor(transformed_data, input_tensor.shape);
}

// New function using CUDA for matrix-vector multiplication
Tensor tensor_matrix_vector_multiply_cuda(const Tensor& matrix_tensor, const Tensor& vector_tensor) {
    if (matrix_tensor.shape.size() != 2 || vector_tensor.shape.size() != 1) {
        throw std::invalid_argument("Matrix-vector multiplication requires a 2D matrix and a 1D vector.");
    }
    size_t m = matrix_tensor.shape[0];
    size_t n = matrix_tensor.shape[1];
    if (n != vector_tensor.shape[0]) {
        throw std::invalid_argument("Matrix columns must equal vector size for multiplication.");
    }
    
    // Create new tensor for the result
    Tensor result_tensor;
    result_tensor.shape = {m};
    result_tensor.data.resize(m);

    // Copy host data to device
    double *d_A, *d_x, *d_y;
    CUDA_CHECK(cudaMalloc(&d_A, m * n * sizeof(double)));
    CUDA_CHECK(cudaMalloc(&d_x, n * sizeof(double)));
    CUDA_CHECK(cudaMalloc(&d_y, m * sizeof(double)));
    
    CUDA_CHECK(cudaMemcpy(d_A, matrix_tensor.data.data(), m * n * sizeof(double), cudaMemcpyHostToDevice));
    CUDA_CHECK(cudaMemcpy(d_x, vector_tensor.data.data(), n * sizeof(double), cudaMemcpyHostToDevice));
    
    // Launch kernel
    int threads_per_block = 256;
    int blocks_per_grid = (m + threads_per_block - 1) / threads_per_block;
    matrixVectorMultiplyKernel<<<blocks_per_grid, threads_per_block>>>(m, n, d_A, d_x, d_y);
    CUDA_CHECK(cudaGetLastError()); // Check for kernel launch errors
    CUDA_CHECK(cudaDeviceSynchronize()); // Wait for kernel to finish

    // Copy result back to host
    CUDA_CHECK(cudaMemcpy(result_tensor.data.data(), d_y, m * sizeof(double), cudaMemcpyDeviceToHost));

    // Clean up device memory
    CUDA_CHECK(cudaFree(d_A));
    CUDA_CHECK(cudaFree(d_x));
    CUDA_CHECK(cudaFree(d_y));
    
    return result_tensor;
}

// --- Workflow Handlers ---
std::vector<double> handle_workflow_json(simdjson::ondemand::document& workflow_doc) {
    using namespace simdjson;
    auto data_store = std::make_unique<std::map<std::string, Tensor>>();
    std::vector<double> final_result_data;

    for (auto& step : workflow_doc.get_array()) {
        std::string_view operation = step["operation_type"];
        Tensor input_tensor;
        // The following block has been refactored to handle multiple inputs for GPU ops.
        std::string_view input_type;
        try { input_type = step["input_data"]["type"]; }
        catch(...) { input_type = "multi"; } // Assume multi-input for new operations

        Tensor input_tensor_2; // Second input for matrix-vector multiplication

        if (operation == "TENSOR_MATRIX_VECTOR_MULTIPLY_CUDA") {
            // Handle multiple inputs for the CUDA operation
            auto matrix_data_source = step["input_data"]["matrix_source"];
            auto vector_data_source = step["input_data"]["vector_source"];
            
            // Resolve matrix input
            if (matrix_data_source["type"] == "direct") {
                std::vector<double> flat_data;
                for (auto val : matrix_data_source["data"].get_array()) { flat_data.push_back(val.get_double()); }
                std::vector<size_t> shape;
                for (auto val : matrix_data_source["shape"].get_array()) { shape.push_back(size_t(val.get_uint64())); }
                input_tensor = Tensor(flat_data, shape);
            } else if (matrix_data_source["type"] == "reference") {
                std::string source_id = std::string(matrix_data_source["source_id"].get_string());
                auto it = data_store->find(source_id);
                if (it != data_store->end()) { input_tensor = it->second; }
                else { throw std::runtime_error("Referenced matrix data not found: " + source_id); }
            }

            // Resolve vector input
            if (vector_data_source["type"] == "direct") {
                std::vector<double> flat_data;
                for (auto val : vector_data_source["data"].get_array()) { flat_data.push_back(val.get_double()); }
                std::vector<size_t> shape;
                for (auto val : vector_data_source["shape"].get_array()) { shape.push_back(size_t(val.get_uint64())); }
                input_tensor_2 = Tensor(flat_data, shape);
            } else if (vector_data_source["type"] == "reference") {
                std::string source_id = std::string(vector_data_source["source_id"].get_string());
                auto it = data_store->find(source_id);
                if (it != data_store->end()) { input_tensor_2 = it->second; }
                else { throw std::runtime_error("Referenced vector data not found: " + source_id); }
            }
        } else {
            // Handle single input for existing operations
            auto input_data = step["input_data"];
            input_type = input_data["type"];

            if (input_type == "direct") {
                if (operation == "INTERPOLATE") {
                    // Handle the complex list of lists structure for interpolation
                    std::map<std::string, std::vector<double>> interpolation_data;
                    auto fx_data_list = input_data["fx_data"].get_array();
                    auto fy_data_list = input_data["fy_data"].get_array();
                    size_t idx = 0;
                    for (auto fx : fx_data_list) {
                        std::vector<double> fx_vec;
                        for (auto val : fx.get_array()) fx_vec.push_back(val.get_double());
                        interpolation_data["fx" + std::to_string(idx)] = std::move(fx_vec);
                        auto fy = fy_data_list.at(idx).get_array();
                        std::vector<double> fy_vec;
                        for (auto val : fy) fy_vec.push_back(val.get_double());
                        interpolation_data["fy" + std::to_string(idx)] = std::move(fy_vec);
                        idx++;
                    }
                    std::vector<double> x_interp;
                    for (auto val : step["parameters"]["x_interp_points"].get_array()) { x_interp.push_back(val.get_double()); }
                    
                    std::vector<double> interp_result = hyperbolic_parabolic_interpolation(interpolation_data, x_interp);
                    input_tensor = Tensor(interp_result, {interp_result.size()});

                } else {
                    std::vector<double> flat_data;
                    for (auto val : input_data["data"].get_array()) { flat_data.push_back(val.get_double()); }
                    std::vector<size_t> shape;
                    for (auto val : input_data["shape"].get_array()) { shape.push_back(size_t(val.get_uint64())); }
                    input_tensor = Tensor(flat_data, shape);
                }
            } else if (input_type == "reference") {
                std::string source_id = std::string(input_data["source_id"].get_string());
                auto it = data_store->find(source_id);
                if (it != data_store->end()) { input_tensor = it->second; }
                else { throw std::runtime_error("Referenced data not found: " + source_id); }
            }
        }

        Tensor result_tensor;
        if (operation == "CALCULATE_GRADIENT_1D") {
            result_tensor = calculate_gradient_1d(input_tensor);
        } else if (operation == "TENSOR_TRANSFORMATION") {
            result_tensor = tensor_transform(input_tensor);
        } else if (operation == "EIGENVALUE_PACKING") {
            std::vector<double> unpacked_data = pack_eigenvalue_data(input_tensor.data);
            result_tensor = Tensor(unpacked_data, input_tensor.shape);
        } else if (operation == "NEUROMORPHIC_PREDICT") {
            SpikingNetwork snn(input_tensor.data.size(), 10, 5);
            std::vector<int> spike_counts = snn.predict(input_tensor.data);
            std::vector<double> spike_double;
            for (int count : spike_counts) spike_double.push_back(static_cast<double>(count));
            result_tensor = Tensor(spike_double, {spike_double.size()});
        } else if (operation == "TENSOR_MATRIX_VECTOR_MULTIPLY_CUDA") {
            result_tensor = tensor_matrix_vector_multiply_cuda(input_tensor, input_tensor_2);
        } else {
            throw std::runtime_error("Unsupported operation: " + std::string(operation));
        }

        auto output_id_res = step["output_id"];
        if (output_id_res.error() == SUCCESS) {
            (*data_store)[std::string(output_id_res.get_string())] = result_tensor;
        } else {
            final_result_data = result_tensor.data;
        }
    }
    return final_result_data;
}

void handle_json_workflow_request(int client_socket, const std::string& payload_json) {
    using namespace simdjson;
    try {
        padded_string padded_payload = padded_string::load(payload_json);
        ondemand::parser parser;
        ondemand::document workflow_doc = parser.iterate(padded_payload);
        std::vector<double> result_data = handle_workflow_json(workflow_doc);

        std::string response = "{ \"status\": \"success\", \"result\": [";
        for (size_t i = 0; i < result_data.size(); ++i) {
            response += std::to_string(result_data[i]);
            if (i < result_data.size() - 1) { response += ", "; }
        }
        response += "] }";
        send(client_socket, response.c_str(), response.length(), 0);
    } catch (const std::exception& e) {
        std::string error_response = "{ \"status\": \"error\", \"message\": \"" + std::string(e.what()) + "\" }";
        send(client_socket, error_response.c_str(), error_response.length(), 0);
    }
    close(client_socket);
}

void handle_legacy_binary(int client_socket, uint8_t initial_op_code) {
    try {
        if (initial_op_code != OPERATION_LEGACY_INTERPOLATE) { send_raw_error(client_socket, "Invalid operation code."); return; }
        uint32_t num_dims;
        if (receive_all(client_socket, &num_dims, sizeof(uint32_t)) <= 0) { send_raw_error(client_socket, "Disconnected during dimension count."); return; }
        num_dims = ntohl(num_dims);
        std::map<std::string, std::vector<double>> data_dict;
        std::vector<double> x_interp;
        for (uint32_t i = 0; i < num_dims; ++i) {
            uint32_t fx_len, fy_len;
            if (receive_all(client_socket, &fx_len, sizeof(uint32_t)) <= 0 ||
                receive_all(client_socket, &fy_len, sizeof(uint32_t)) <= 0) { send_raw_error(client_socket, "Disconnected during length reception."); return; }
            fx_len = ntohl(fx_len); fy_len = ntohl(fy_len);
            std::vector<double> fx_data(fx_len);
            std::vector<double> fy_data(fy_len);
            if (receive_all(client_socket, fx_data.data(), fx_len * sizeof(double)) <= 0 ||
                receive_all(client_socket, fy_data.data(), fy_len * sizeof(double)) <= 0) { send_raw_error(client_socket, "Incomplete data."); return; }
            data_dict["fx" + std::to_string(i)] = fx_data;
            data_dict["fy" + std::to_string(i)] = fy_data;
        }
        uint32_t x_interp_len;
        if (receive_all(client_socket, &x_interp_len, sizeof(uint32_t)) <= 0) { send_raw_error(client_socket, "Disconnected during interp length."); return; }
        x_interp_len = ntohl(x_interp_len);
        x_interp.resize(x_interp_len);
        if (receive_all(client_socket, x_interp.data(), x_interp_len * sizeof(double)) <= 0) { send_raw_error(client_socket, "Incomplete interp data."); return; }
        std::vector<double> result = hyperbolic_parabolic_interpolation(data_dict, x_interp);
        send_raw_result(client_socket, result);
    } catch (const std::exception& e) {
        send_raw_error(client_socket, e.what());
    }
    close(client_socket);
}

void handle_client(int client_socket) {
    uint8_t op_code_buffer[1];
    ssize_t bytes_peeked = recv(client_socket, op_code_buffer, 1, MSG_PEEK);
    if (bytes_peeked <= 0) { close(client_socket); return; }
    uint8_t op_code = op_code_buffer[0];
    recv(client_socket, op_code_buffer, 1, 0);
    if (op_code == OPERATION_WORKFLOW) {
        uint32_t payload_len;
        if (receive_all(client_socket, &payload_len, sizeof(payload_len)) <= 0) { close(client_socket); return; }
        payload_len = ntohl(payload_len);
        std::string payload(payload_len, '\0');
        if (receive_all(client_socket, &payload[0], payload_len) <= 0) { close(client_socket); return; }
        handle_json_workflow_request(client_socket, payload);
    } else {
        handle_legacy_binary(client_socket, op_code);
    }
}

void start_unified_server() {
    int server_fd, client_socket;
    struct sockaddr_in address;
    int addrlen = sizeof(address);
    if ((server_fd = socket(AF_INET, SOCK_STREAM, 0)) == 0) { perror("Socket creation failed"); return; }
    int opt = 1;
    if (setsockopt(server_fd, SOL_SOCKET, SO_REUSEADDR, &opt, sizeof(opt)) < 0) {
        perror("setsockopt");
        close(server_fd);
        return;
    }
    address.sin_family = AF_INET;
    address.sin_addr.s_addr = INADDR_ANY;
    address.sin_port = htons(LEGACY_SERVER_PORT);
    if (bind(server_fd, (struct sockaddr *)&address, sizeof(address)) < 0) { perror("Bind failed"); return; }
    if (listen(server_fd, 5) < 0) { perror("Listen failed"); return; }
    std::cout << "Unified server listening on port " << LEGACY_SERVER_PORT << std::endl;
    while (true) {
        if ((client_socket = accept(server_fd, (struct sockaddr *)&address, (socklen_t*)&addrlen)) < 0) { perror("Accept failed"); continue; }
        std::thread client_thread(handle_client, client_socket);
        client_thread.detach();
    }
}

int main() {
    start_unified_server();
    return 0;
}