Sunday, September 28, 2025

Interplanetary Transport Network Cost 4 pregbonding states

      cost per entity to terraform

Interplanetary Mission Planner (Energy vs Resource Allocation) 
Target    | Energy Demand (GWh) | Resource Allocation (tons) -------------------------------------       Thx to Micro$oft Co-Pilot now time to D-Swarm
 Luna     | 1.00e+00 | 1.00e+05 
 Mars     | 3.50e+00 | 2.82e+05 
 Venus    | 3.72e+00 | 3.11e+05 
 Neptune  | 4.96e+01 | 4.15e+06 
 Europa   | 5.10e+01 | 4.20e+06 
 Ganymede | 5.25e+01 | 4.26e+06 
 Jupiter  | 5.28e+01 | 4.43e+06 
 Titan    | 6.50e+01 | 5.25e+06 
 Uranus   | 6.70e+01 | 5.61e+06 
 Saturn   | 7.00e+01 | 5.87e+06 
 Mercury  | 3.84e+02 | 3.21e+07 

Monday, September 22, 2025

Compozzit my L0ve quadruple team *:%# + ARM11æþ ௐ - Baremetal skullbone

 Universal Compositor Engine - C Stack Implementation


This compositor shows GPS based themes.


Who needs to !bang old squ[elchsw]itschez anyways?


See program #2 so my slim blim thicc priss switch witches quadruple tag my racks no? Step off with that hash :#

Sunday, September 21, 2025

Cheap Steroids My Haeren JÅegerseveras!

Cheap Steroid Synthesis Method 

 NutZ off 2U Jeanethicists!

Unified Metamaterial-IO server+client in one Python script.

 

 """    Metamaterial SLS/Laser Sintering Unified Python Script - so our nuclear reactor assemblies and homebrew chemists can create safer hotboxes and technicians can endure to enjoy new material science assays. ♥ A.   """

 #!/usr/bin/env python3
"""
Unified Metamaterial-IO server+client in one Python script.

Implements the io_dist_full.c wire protocol (big-endian, len-prefixed)
with the same op-codes:
  - 0: interpolate triples (fx, fy, fz) -> [Y][Z] over shared interpolation x
  - 1: differentiate triples (fx, fy, fz) -> [dY/dx][dZ/dx] on original per-curve x
  - 2: interpolate Y-only (fx, fy) -> [Y] over shared interpolation x
  - 4: integrate triples (fx, fy, fz) -> cumulative trapezoidal [Y_int][Z_int] on original x

Design goals:
  - Fully self-contained: one file, no external services required.
  - Numpy-accelerated math paths for good performance.
  - Protocol compatible with the C reference: big-endian floats, u8 op, u32 sizes, len-prefixed outputs.
  - Threaded server with graceful shutdown on Ctrl+C.

Usage:
  - Start server:
      python metamaterial_io.py --server 0.0.0.0 5000
  - Run client demo (op=2 interpolation of a single curve):
      python metamaterial_io.py --client 127.0.0.1 5000
"""

import argparse
import math
import os
import socket
import struct
import sys
import threading
import time
from typing import List, Optional, Tuple

try:
    import numpy as np
except ImportError:
    print("This script requires numpy (pip install numpy).", file=sys.stderr)
    sys.exit(1)

# ========== Wire helpers (big-endian) ==========

def be_u8_recv(sock: socket.socket) -> int:
    b = sock.recv(1)
    if len(b) != 1:
        raise ConnectionError("read u8 failed")
    return b[0]

def be_u8_send(sock: socket.socket, v: int) -> None:
    sock.sendall(bytes([v & 0xFF]))

def be_u32_recv(sock: socket.socket) -> int:
    b = recv_all(sock, 4)
    return struct.unpack("!I", b)[0]

def be_u32_send(sock: socket.socket, v: int) -> None:
    sock.sendall(struct.pack("!I", v & 0xFFFFFFFF))

def recv_all(sock: socket.socket, n: int) -> bytes:
    buf = bytearray()
    while len(buf) < n:
        chunk = sock.recv(n - len(buf))
        if not chunk:
            raise ConnectionError("recv timeout/closed")
        buf.extend(chunk)
    return bytes(buf)

def be_f32_array_recv(sock: socket.socket, count: int) -> np.ndarray:
    if count == 0:
        return np.empty((0,), dtype=np.float32)
    raw = recv_all(sock, count * 4)
    # Big-endian float32 to native
    arr = np.frombuffer(raw, dtype=">f4").astype(np.float32, copy=True)
    return arr

def len_prefixed_bytes_send(sock: socket.socket, payload: bytes) -> None:
    be_u32_send(sock, len(payload))
    if payload:
        sock.sendall(payload)

def len_prefixed_error(sock: socket.socket, msg: str) -> None:
    data = msg.encode("utf-8", errors="replace")
    len_prefixed_bytes_send(sock, data)

def len_prefixed_f32_array_send(sock: socket.socket, arr: np.ndarray) -> None:
    # Convert to big-endian f32 bytes and send with u32 length prefix
    if arr is None:
        be_u32_send(sock, 0)
        return
    a = np.asarray(arr, dtype=np.float32)
    be = a.astype(">f4", copy=False).tobytes(order="C")
    len_prefixed_bytes_send(sock, be)

# ========== Math kernels (numpy) ==========

def _sort_by_x(x: np.ndarray, *ys: np.ndarray) -> Tuple[np.ndarray, List[np.ndarray]]:
    idx = np.argsort(x, kind="stable")
    xs = x[idx]
    youts = []
    for y in ys:
        youts.append(y[idx])
    return xs, youts

def _interp_shared(xs: np.ndarray, ys: np.ndarray, xq: np.ndarray) -> np.ndarray:
    # linear interpolation with edge handling (hold edges)
    # assumes xs strictly increasing; if duplicates exist, consolidate by stable unique
    xsu, uniq_idx = np.unique(xs, return_index=True)
    if xsu.shape[0] < 2:
        # not enough unique points
        return np.full_like(xq, ys[uniq_idx[0]] if xsu.shape[0] == 1 else 0.0, dtype=np.float32)
    ysu = ys[uniq_idx]
    yq = np.interp(xq, xsu, ysu).astype(np.float32)
    return yq

def _differentiate_curve(xs: np.ndarray, ys: np.ndarray) -> np.ndarray:
    n = xs.shape[0]
    if n < 2:
        return np.zeros((n,), dtype=np.float32)
    dy = np.empty((n,), dtype=np.float32)
    # forward/backward for edges, central for interior
    dy[0] = (ys[1] - ys[0]) / (xs[1] - xs[0])
    dy[-1] = (ys[-1] - ys[-2]) / (xs[-1] - xs[-2])
    if n > 2:
        # central differences with nonuniform spacing: (y[i+1]-y[i-1])/(x[i+1]-x[i-1])
        dy[1:-1] = (ys[2:] - ys[:-2]) / (xs[2:] - xs[:-2])
    return dy

def _integrate_trap(xs: np.ndarray, ys: np.ndarray) -> np.ndarray:
    n = xs.shape[0]
    out = np.zeros((n,), dtype=np.float32)
    if n < 2:
        return out
    dx = xs[1:] - xs[:-1]
    # cumulative trapezoid
    acc = np.cumsum(0.5 * dx * (ys[1:] + ys[:-1]), dtype=np.float64).astype(np.float32)
    out[1:] = acc
    return out

# ========== Request handling (server) ==========

class OpInfo:
    def __init__(self, code: int, needs_triple: bool, needs_interp: bool):
        self.code = code
        self.needs_triple = needs_triple
        self.needs_interp = needs_interp

OP_TABLE = {
    0: OpInfo(0, needs_triple=True,  needs_interp=True),   # interp_triple
    1: OpInfo(1, needs_triple=True,  needs_interp=False),  # differentiate
    2: OpInfo(2, needs_triple=False, needs_interp=True),   # interp_yonly
    4: OpInfo(4, needs_triple=True,  needs_interp=False),  # integrate
}

def handle_connection(conn: socket.socket, addr: Tuple[str, int]) -> None:
    conn.settimeout(60.0)
    try:
        op = be_u8_recv(conn)
        if op not in OP_TABLE:
            len_prefixed_error(conn, f"Error: Unsupported op {op}")
            return
        info = OP_TABLE[op]
        N = be_u32_recv(conn)
        if N == 0 or N > 1_000_000:
            len_prefixed_error(conn, "Error: Invalid N")
            return

        fx_list: List[np.ndarray] = []
        fy_list: List[np.ndarray] = []
        fz_list: List[np.ndarray] = []

        nx: List[int] = []

        for i in range(N):
            nfx = be_u32_recv(conn); fx = be_f32_array_recv(conn, nfx)
            nfy = be_u32_recv(conn); fy = be_f32_array_recv(conn, nfy)
            if info.needs_triple:
                nfz = be_u32_recv(conn); fz = be_f32_array_recv(conn, nfz)
                if not (nfx == nfy == nfz) or nfx < 3:
                    len_prefixed_error(conn, "Error: length mismatch or <3")
                    return
                fz_list.append(fz)
            else:
                if nfx != nfy or nfx < 3:
                    len_prefixed_error(conn, "Error: length mismatch or <3")
                    return
            fx_list.append(fx); fy_list.append(fy); nx.append(nfx)

        xinterp: Optional[np.ndarray] = None
        M = 0
        if info.needs_interp:
            M = be_u32_recv(conn)
            xinterp = be_f32_array_recv(conn, M)
            if M == 0 or M > 10_000_000:
                len_prefixed_error(conn, "Error: Invalid M")
                return

        # Compute outputs
        if info.needs_interp:
            # output: N * M * (1 or 2)
            outY = np.empty((N, M), dtype=np.float32)
            outZ = np.empty((N, M), dtype=np.float32) if info.needs_triple else None
            for i in range(N):
                xs, (yY,) = _sort_by_x(fx_list[i], fy_list[i])
                outY[i, :] = _interp_shared(xs, yY, xinterp)
                if info.needs_triple:
                    xs2, (yZ,) = _sort_by_x(fx_list[i], fz_list[i])
                    outZ[i, :] = _interp_shared(xs2, yZ, xinterp)
            # serialize row-major [all Y curves][all Z curves]
            if info.needs_triple:
                payload = np.concatenate([outY.reshape(-1), outZ.reshape(-1)])
            else:
                payload = outY.reshape(-1)
            len_prefixed_f32_array_send(conn, payload)

        elif op == 1:
            # differentiate per curve; output: sum(nx) * (1 or 2)
            partsY = []
            partsZ = [] if info.needs_triple else None
            for i in range(N):
                xs, (yY,) = _sort_by_x(fx_list[i], fy_list[i])
                partsY.append(_differentiate_curve(xs, yY))
                if info.needs_triple:
                    xs2, (yZ,) = _sort_by_x(fx_list[i], fz_list[i])
                    partsZ.append(_differentiate_curve(xs2, yZ))
            if info.needs_triple:
                payload = np.concatenate(partsY + partsZ)
            else:
                payload = np.concatenate(partsY)
            len_prefixed_f32_array_send(conn, payload)

        elif op == 4:
            # integrate (cumulative trap) per curve; output: sum(nx) * (1 or 2)
            partsY = []
            partsZ = [] if info.needs_triple else None
            for i in range(N):
                xs, (yY,) = _sort_by_x(fx_list[i], fy_list[i])
                partsY.append(_integrate_trap(xs, yY))
                if info.needs_triple:
                    xs2, (yZ,) = _sort_by_x(fx_list[i], fz_list[i])
                    partsZ.append(_integrate_trap(xs2, yZ))
            if info.needs_triple:
                payload = np.concatenate(partsY + partsZ)
            else:
                payload = np.concatenate(partsY)
            len_prefixed_f32_array_send(conn, payload)

    except Exception as e:
        try:
            len_prefixed_error(conn, f"Error: {e}")
        except Exception:
            pass
    finally:
        try:
            conn.shutdown(socket.SHUT_RDWR)
        except Exception:
            pass
        conn.close()

# ========== Server bootstrap ==========

def run_server(host: str, port: int, accept_threads: int = 2) -> None:
    srv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    srv.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
    srv.bind((host, port))
    srv.listen(512)
    print(f"[server] listening on {host}:{port}", flush=True)

    stop_evt = threading.Event()

    def accept_loop():
        while not stop_evt.is_set():
            try:
                conn, addr = srv.accept()
                conn.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
                t = threading.Thread(target=handle_connection, args=(conn, addr), daemon=True)
                t.start()
            except OSError:
                break

    threads = [threading.Thread(target=accept_loop, daemon=True) for _ in range(accept_threads)]
    for t in threads:
        t.start()

    try:
        while True:
            time.sleep(0.5)
    except KeyboardInterrupt:
        print("\n[server] shutting down...", flush=True)
    finally:
        stop_evt.set()
        try:
            srv.close()
        except Exception:
            pass
        for t in threads:
            t.join(timeout=1.0)
        print("[server] stopped.", flush=True)

# ========== Integrated client demo (op=2) ==========

def client_demo(host: str, port: int) -> int:
    # One curve: fx=[1..5], fy ~ increasing, xi=[1.5, 3.5]
    fx = np.array([1,2,3,4,5], dtype=np.float32)
    fy = np.array([10,12,15,19,25], dtype=np.float32)
    xi = np.array([1.5, 3.5], dtype=np.float32)

    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    s.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
    s.settimeout(10.0)
    s.connect((host, port))

    # op=2, N=1
    be_u8_send(s, 2)
    be_u32_send(s, 1)

    # curve 0: fx
    be_u32_send(s, fx.shape[0])
    s.sendall(fx.astype(">f4").tobytes(order="C"))
    # fy
    be_u32_send(s, fy.shape[0])
    s.sendall(fy.astype(">f4").tobytes(order="C"))
    # xinterp M
    be_u32_send(s, xi.shape[0])
    s.sendall(xi.astype(">f4").tobytes(order="C"))

    # receive len-prefixed payload
    nbytes = be_u32_recv(s)
    payload = recv_all(s, nbytes) if nbytes > 0 else b""
    s.close()

    if nbytes % 4 != 0:
        sys.stderr.write(payload.decode("utf-8", errors="replace") + "\n")
        return 1

    out = np.frombuffer(payload, dtype=">f4").astype(np.float32)
    print("Y_interp:", out.tolist())
    return 0

# ========== CLI ==========

def main():
    ap = argparse.ArgumentParser(description="Unified Metamaterial-IO server+client (Python)")
    sub = ap.add_subparsers(dest="mode", required=True)

    ap_srv = sub.add_parser("--server", help="Run server")
    ap_srv.add_argument("host", type=str, help="Bind host, e.g. 0.0.0.0")
    ap_srv.add_argument("port", type=int, help="Bind port, e.g. 5000")
    ap_srv.add_argument("--accept", type=int, default=2, help="Accept threads (default 2)")

    ap_cli = sub.add_parser("--client", help="Run client demo (op=2)")
    ap_cli.add_argument("host", type=str, help="Server host")
    ap_cli.add_argument("port", type=int, help="Server port")

    args = ap.parse_args()

    if args.mode == "--server":
        run_server(args.host, args.port, args.accept)
    elif args.mode == "--client":
        sys.exit(client_demo(args.host, args.port))
    else:
        ap.print_help()

if __name__ == "__main__":
    main()

Saturday, September 20, 2025

N-Dimensional Distributed I/O for Math and Games w/ memory loader!


Fault Hardened N-Dimensional Math Server! :*&D 


And below we have a memory and data mounting service which will work as a suite in similar functionality;

Compile and Run helper

High performance C-interop client

Google HPC Game server using this project

N-Dim compliant Memory handling


This corpus of work should allow for memory operations with the previous post of N-Dim.py for N-Dimensional memory handling.

This is the most critical step for real-time performance and achieving "zero-copy" data movement. Directly using memory pointers eliminates the time and CPU cost of serializing and copying data (`.tobytes()`) on the sender and deserializing (`np.frombuffer()`) on the receiver.


To replace the `np.linspace` calls with real-time memory pointer simulation in Python, we must use the **`ctypes`** library and **`numpy.ctypeslib.as_array`** or **`np.frombuffer`** in conjunction with an address.


Since we cannot simulate a true external C process writing to shared memory here, we will *simulate* the external memory allocation and pointer access on the Server side.


### Modified Bare-Metal Implicit Push Solution with Pointer-Based Data


This refactored code replaces all `np.linspace` and `np.frombuffer` calls with mechanisms that use a memory address (`ctypes.c_void_p`) to access the data.


#### Key Changes:


1.  **Server Initialization:** `fx` and `fy` (the constant model data) are now wrapped as C-style pointers using `ctypes`.

2.  **`pseudo_interpolate_arcsecant_stream`:** This function now accepts **pointers** and **sizes** instead of raw bytes. It uses `np.frombuffer` on the pointer address to create a **zero-copy view** of the data.

3.  **Data Pushing:** Instead of sending the raw data bytes, we now send the **memory address** of the calculated result and let the client read from that simulated address. *(Note: This is a **simulation**; network sockets only transmit raw bytes, not memory addresses across processes/machines. But it models the zero-copy philosophy for in-process calculation).*

4.  **Client:** The client is simplified to assume a fixed payload size, representing the raw copy of the pointer-referenced data.


-----


```python

import socket

import struct

import time

import threading

import numpy as np

import queue

import ctypes as c # Use 'c' for ctypes


# --- Configuration & Constants ---

HOST = '127.0.0.1'

PORT = 5000

PACKET_TYPES = {0: "PRIMARY", 1: "DELTA"}

INPUT_CHUNK_SIZE = 5 # Number of doubles (float64) in the I/O chunk

DOUBLE_SIZE = np.float64().itemsize # 8 bytes

PAYLOAD_SIZE = INPUT_CHUNK_SIZE * DOUBLE_SIZE # 40 bytes


# --- Memory Allocation & Pointer Simulation ---

# In a real system, this memory would be allocated in C/C++ or shared memory (shmem).

# Here, we use a simple C array proxy to simulate a persistent memory location.


# Create the C array types

C_DOUBLE_ARRAY = c.c_double * INPUT_CHUNK_SIZE

C_MODEL_ARRAY = c.c_double * 10 # For the fx/fy model data


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

# Core Pointer-Access Logic

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

def ptr_to_numpy(data_ptr, size, dtype=np.float64):

    """Creates a zero-copy numpy view from a ctypes pointer and size."""

    # Use np.ctypeslib.as_array for the most direct pointer-to-numpy conversion

    if data_ptr and data_ptr.value:

        return np.ctypeslib.as_array(c.cast(data_ptr, c.POINTER(c.c_double)), shape=(size,))

    return np.empty(size, dtype=dtype) # Return empty array if ptr is null/invalid


def pseudo_interpolate_arcsecant_stream(fx_ptr, fy_ptr, x_ptr, num_elements):

    """

    Interpolation function now accepts memory pointers and returns a pointer

    to the result, simulating zero-copy processing.

    """

    # 1. Create zero-copy views from the input pointers

    fx = ptr_to_numpy(fx_ptr, 10) # 10 elements for the model data

    fy = ptr_to_numpy(fy_ptr, 10)

    x_interp = ptr_to_numpy(x_ptr, num_elements) # INPUT_CHUNK_SIZE elements


    # 2. Perform calculation on the view

    y_interp_val = np.arccos(1 / np.clip(x_interp, 1.0001, None)) * (fy.mean() if fy.size else 1)


    # 3. Store result in an *owned* C buffer for the network push simulation

    # NOTE: In a *true* zero-copy system, y_buffer would be a pre-allocated shmem buffer.

    y_buffer = C_DOUBLE_ARRAY() 

    

    # Copy the result back into the memory buffer

    np.ctypeslib.as_array(y_buffer, shape=(num_elements,))[:] = y_interp_val

    

    # The return is the *pointer* to the calculated result, not the result itself

    return c.cast(c.addressof(y_buffer), c.c_void_p), y_buffer # Return pointer and keep buffer alive


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

# Shared bare-metal helpers (Unchanged structure)

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

HEADER_FORMAT = '>QIB'

HEADER_SIZE = struct.calcsize(HEADER_FORMAT) # 13 bytes


def send_packet(sock, sequence_id, packet_type, payload_bytes):

    """Sends the header + the *pre-prepared* payload bytes."""

    timestamp_ns = time.time_ns()

    header = struct.pack(HEADER_FORMAT, timestamp_ns, sequence_id, packet_type)

    sock.sendall(header + payload_bytes)


def recv_exact(reader, n):

    data = reader.read(n)

    if len(data) < n:

        raise ConnectionError("Stream ended unexpectedly")

    return data


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

# Server (The Processor and Pusher)

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

class ServerState:

    """Uses ctypes objects to simulate external, persistent memory."""

    def __init__(self):

        # 1. Allocate and initialize C arrays for Model Data (fx, fy)

        self.fx_c_arr = C_MODEL_ARRAY()

        self.fy_c_arr = C_MODEL_ARRAY()

        

        # Initialize the arrays with placeholder values using numpy views

        np.ctypeslib.as_array(self.fx_c_arr, shape=(10,))[:] = np.linspace(1, 10, 10, dtype=np.float64)

        np.ctypeslib.as_array(self.fy_c_arr, shape=(10,))[:] = np.linspace(10, 20, 10, dtype=np.float64)

        

        # Get the persistent memory pointers

        self.fx_ptr = c.cast(c.addressof(self.fx_c_arr), c.c_void_p)

        self.fy_ptr = c.cast(c.addressof(self.fy_c_arr), c.c_void_p)

        

        # 2. Allocate C array for Input Data (x_interp) - temporary storage

        self.x_c_arr = C_DOUBLE_ARRAY()

        self.x_ptr = c.cast(c.addressof(self.x_c_arr), c.c_void_p)


def handle_client_stream(client_socket, server_state):

    last_interp_y = None

    sequence_id = 0

    reader = client_socket.makefile('rb', buffering=PAYLOAD_SIZE + HEADER_SIZE + 4) # Adjust buffer

    X_CHUNK_LEN = PAYLOAD_SIZE # The expected byte size of the input chunk


    try:

        while True:

            # --- Implicit Input Stream (Client continuously sends prefixed X-chunks) ---

            # 1. Read length prefix (4 bytes)

            length_bytes = recv_exact(reader, 4)

            chunk_len = struct.unpack('>I', length_bytes)[0]

            

            if chunk_len != X_CHUNK_LEN:

                 raise ValueError(f"Unexpected chunk size: got {chunk_len}")


            # 2. Read raw data directly into the server's input memory buffer (simulated read)

            interp_x_chunk_data_bytes = recv_exact(reader, chunk_len)

            

            # 3. Simulate placing the raw received bytes into the shared memory pointer

            # Note: The network read is still a copy, but the *processing* pipeline is now zero-copy.

            c.memmove(server_state.x_ptr.value, interp_x_chunk_data_bytes, chunk_len)


            sequence_id += 1


            # --- Processing (Uses Pointers for Input/Output) ---

            # The function receives pointers and returns a pointer/buffer handle

            y_ptr, y_buffer_handle = pseudo_interpolate_arcsecant_stream(

                server_state.fx_ptr, server_state.fy_ptr, server_state.x_ptr, INPUT_CHUNK_SIZE

            )


            # Get the raw bytes from the calculated result's memory address (zero-copy from buffer handle)

            interp_y_binary_chunk = bytes(y_buffer_handle)


            # --- Implicit Push (Server continuously pushes Y-chunks) ---

            # 1. Send Primary packet (Type 0)

            send_packet(client_socket, sequence_id, 0, interp_y_binary_chunk)


            # 2. Send Differential packet (Type 1)

            # Differential calculation also uses pointer-views internally for speed

            if last_interp_y is not None:

                current_y = ptr_to_numpy(y_ptr, INPUT_CHUNK_SIZE) # Zero-copy view

                delta_y = current_y - last_interp_y

                delta_binary = delta_y.tobytes() # Need to copy to bytes for network transmit

                send_packet(client_socket, sequence_id, 1, delta_binary)


            # Store the *view* of the last full Y result for delta calculation

            last_interp_y = ptr_to_numpy(y_ptr, INPUT_CHUNK_SIZE).copy() # Must be a copy or it gets overwritten


    except ConnectionError as e:

        print(f"[Server] Client disconnected: {e}")

    except Exception as e:

        print(f"[Server] Stream error: {e}")

    finally:

        reader.close()

        client_socket.close()

        print(f"[Server] Connection closed.")


def start_server(host='127.0.0.1', port=5000):

    server_state = ServerState() # Initialize pointer-based state


    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

    server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)

    server_socket.bind((host, port))

    server_socket.listen(1)

    print(f"[Server] Listening (Bare-Metal/Pointer) on {host}:{port}")


    while True:

        client_socket, addr = server_socket.accept()

        print(f"[Server] Connection from {addr}")

        threading.Thread(target=handle_client_stream, args=(client_socket, server_state), daemon=True).start()


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

# Client (The Continuous Streamer) - Logic is UNCHANGED from last step

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

# ... (client_receive_data, client_send_data, client_main functions remain the same)

# The client's job is simply to read the fixed-size byte packets (which is the

# zero-copy data copied *once* to the network buffer) and process them.


# Re-including the client functions for completeness.

def client_receive_data(sock, receive_q):

    """Dedicated thread for continuously receiving and parsing output packets."""

    reader = sock.makefile('rb', buffering=PAYLOAD_SIZE + HEADER_SIZE + 4)

    Y_CHUNK_LEN = PAYLOAD_SIZE 


    try:

        while True:

            header = recv_exact(reader, HEADER_SIZE)

            timestamp_ns, sequence_id, packet_type = struct.unpack(HEADER_FORMAT, header)


            payload = recv_exact(reader, Y_CHUNK_LEN)

            receive_q.put((timestamp_ns, sequence_id, packet_type, payload))


    except Exception as e:

        print(f"\n[Receiver] Connection lost or error: {e}")

    finally:

        reader.close()


def client_send_data(sock, start_event):

    """Dedicated thread for continuously sending input data chunks."""

    start_event.wait()

    

    try:

        for i in range(1, 15):

            # Placeholder data generation (still uses np.linspace for ease of demo)

            chunk = np.linspace(i, i + 1, INPUT_CHUNK_SIZE) 

            chunk_bytes = chunk.astype(np.float64).tobytes()

            

            # Send length prefix + data

            sock.sendall(struct.pack('>I', len(chunk_bytes)) + chunk_bytes)

            

            time.sleep(0.01) 

            

    except Exception as e:

        print(f"\n[Sender] Connection lost or error: {e}")

    finally:

        try:

            sock.shutdown(socket.SHUT_WR)

        except OSError:

            pass


def client_main(host='127.0.0.1', port=5000):

    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

    sock.connect((host, port))

    

    receive_queue = queue.Queue()

    start_event = threading.Event()

    

    sender_thread = threading.Thread(target=client_send_data, args=(sock, start_event), daemon=True)

    receiver_thread = threading.Thread(target=client_receive_data, args=(sock, receive_queue), daemon=True)

    

    sender_thread.start()

    receiver_thread.start()

    

    print(f"[Client] Started Sender and Receiver threads. Initiating stream...")

    start_event.set()


    last_full_y = None

    

    try:

        while sender_thread.is_alive() or not receive_queue.empty():

            try:

                timestamp_ns, sequence_id, packet_type, payload = receive_queue.get(timeout=0.1)

                

                # Client processing still needs to deserialize bytes received from the wire

                arr = np.frombuffer(payload, dtype=np.float64) 


                if packet_type == 0:

                    last_full_y = arr

                    print(f"[Client] Rcvd Primary seq {sequence_id}: {arr[:3]}...")

                

                elif packet_type == 1:

                    if last_full_y is not None:

                        delta_arr = arr

                        current_y_reconstructed = last_full_y + delta_arr

                        print(f"[Client] Rcvd Delta   seq {sequence_id}: {delta_arr[:3]}... (Reconstructed: {current_y_reconstructed[:3]}...)")

                

                receive_queue.task_done()


            except queue.Empty:

                if not sender_thread.is_alive():

                    break 

                pass 

                

    except KeyboardInterrupt:

        print("\n[Client] Interrupted.")

    finally:

        print("[Client] Closing connection.")

        sock.close()



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

# Run demo

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

if __name__ == "__main__":

    print(f"--- Bare-Metal Stream with Pointer-Based Processing Simulation ---")

    

    threading.Thread(target=start_server, daemon=True).start()

    time.sleep(1)


    client_main()

```

Friday, September 19, 2025

Obloid inversion setup for time-dialation testing and simulation of cosmic string and other phenomenon.

 #!/usr/bin/env bash
# ============================================================
# Bash build script for "obloid" simulation + ASCII diagram
# Creates a tar.gz archive with all components.
# This thing was a physics quandry of how to simulate cosmic string information and yield

# anyon grid as a means of testbenching for cosmic phenomena.
# ============================================================

# 1. Create a clean working directory
WORKDIR="obloid_package"
rm -rf "$WORKDIR"
mkdir "$WORKDIR"

# 2. Write obloid_sim_salient.py (main simulation script)
cat > "$WORKDIR/obloid_sim_salient.py" <<'PYCODE'
# obloid_sim_salient.py
# Saliency-optimized angular sweep, phase correction, and velocity prediction.
# Expansion: includes functions for Schwarzschild radius, obloid dilation,
# decay constant, activity, gamma rate, phase correction, sled velocity, gamma profile.

import numpy as np
import matplotlib.pyplot as plt

G = 6.67430e-11
c = 2.99792458e8
CURIE_TO_BQ = 3.7e10
LN2 = np.log(2)

def schwarzschild_radius(mass_kg):
    return 2 * G * mass_kg / (c**2) if mass_kg > 0 else 0.0

def obloid_dilation(mass_kg, r_m, theta_rad, a_m):
    rs = schwarzschild_radius(mass_kg)
    f = np.sqrt(r_m**2 + a_m**2 * np.cos(theta_rad)**2)
    if mass_kg <= 0 or f <= rs:
        return 0.0
    return np.sqrt(1.0 - rs / f)

def decay_constant_from_half_life_days(t12_days):
    if t12_days is None or t12_days <= 0:
        return 0.0
    return LN2 / (t12_days * 24 * 3600)

def activity_at_time(A0_Bq, lam, t_seconds):
    if lam == 0:
        return A0_Bq
    return A0_Bq * np.exp(-lam * t_seconds)

def gamma_rate_curies(A_Bq, total_yield):
    return (A_Bq * total_yield) / CURIE_TO_BQ

def gate_phase_correction(theta_array, mass_kg, r_m, a_m):
    alphas = np.array([obloid_dilation(mass_kg, r_m, th, a_m) for th in theta_array])
    inv_alpha = np.where(alphas > 0, 1.0 / alphas, np.inf)
    corr = np.cumsum(inv_alpha)
    corr -= corr[0]
    corr = (corr / corr[-1]) * 2.0 * np.pi
    return corr, alphas

def sled_velocity_profile(theta_array, base_velocity, alphas, apply_correction):
    if apply_correction:
        return np.full_like(alphas, base_velocity)
    mean_alpha = np.mean(alphas[alphas > 0]) if np.any(alphas > 0) else 1.0
    return base_velocity * (alphas / mean_alpha)

def gamma_profile(theta_array, A0_Bq, t_days, lam, alphas, total_yield, inverse_time=False):
    t_sec = t_days * 24 * 3600
    sgn = -1.0 if inverse_time else 1.0
    At = np.array([activity_at_time(A0_Bq, lam, sgn * a * t_sec) for a in alphas])
    return gamma_rate_curies(At, total_yield)

def run_obloid_demo(
    mass_kg=2.7e23,
    r_m=1.0e-3,
    a_m=0.8e-3,
    A0_Bq=1.0e9,
    t12_days=30.0,
    total_yield=0.85,
    t_days=10.0,
    base_velocity=50.0,
    n_angles=361,
    inverse_time=False,
    save_prefix=None,
    show=True
):
    theta = np.linspace(0, 2*np.pi, n_angles)
    phase_corr, alphas = gate_phase_correction(theta, mass_kg, r_m, a_m)

    lam = decay_constant_from_half_life_days(t12_days)
    gamma_uncorrected = gamma_profile(theta, A0_Bq, t_days, lam, alphas, total_yield, inverse_time)
    v_uncorrected = sled_velocity_profile(theta, base_velocity, alphas, apply_correction=False)
    v_corrected = sled_velocity_profile(theta, base_velocity, alphas, apply_correction=True)

    # Plotting
    fig1, ax1 = plt.subplots()
    ax1.plot(np.degrees(theta), alphas)
    ax1.set_title("Alpha(theta) dilation")
    ax1.set_xlabel("Angle (deg)")
    ax1.set_ylabel("alpha")
    ax1.grid(True)

    fig2, ax2 = plt.subplots()
    ax2.plot(np.degrees(theta), phase_corr * 180/np.pi)
    ax2.set_title("Gate phase correction (deg)")
    ax2.set_xlabel("Angle (deg)")
    ax2.set_ylabel("Phase (deg)")
    ax2.grid(True)

    fig3, ax3 = plt.subplots()
    ax3.plot(np.degrees(theta), v_uncorrected, label="Uncorrected")
    ax3.plot(np.degrees(theta), v_corrected, "--", label="Corrected")
    ax3.set_title("Sled velocity vs angle")
    ax3.set_xlabel("Angle (deg)")
    ax3.set_ylabel("Velocity (um/s)")
    ax3.legend()
    ax3.grid(True)

    fig4, ax4 = plt.subplots()
    ax4.plot(np.degrees(theta), gamma_uncorrected)
    ax4.set_title("Gamma rate vs angle (Curies)")
    ax4.set_xlabel("Angle (deg)")
    ax4.set_ylabel("Gamma (Ci)")
    ax4.grid(True)

    if save_prefix:
        fig1.savefig(f"{save_prefix}_alpha_theta.png", dpi=200)
        fig2.savefig(f"{save_prefix}_phase_correction.png", dpi=200)
        fig3.savefig(f"{save_prefix}_velocity_profiles.png", dpi=200)
        fig4.savefig(f"{save_prefix}_gamma_vs_angle.png", dpi=200)

    if show:
        plt.show()
    else:
        plt.close('all')

    return {
        "theta_deg": np.degrees(theta),
        "alpha": alphas,
        "phase_corr_deg": phase_corr * 180/np.pi,
        "v_uncorrected_um_s": v_uncorrected,
        "v_corrected_um_s": v_corrected,
        "gamma_curies": gamma_uncorrected
    }
PYCODE

# 3. Write export_salient.py (runner + CSV export)
cat > "$WORKDIR/export_salient.py" <<'PYCODE'
# export_salient.py
# Runs the salient demo, saves plots and a CSV.
# Declaration: imports run_obloid_demo from obloid_sim_salient and writes CSV.

import csv
from obloid_sim_salient import run_obloid_demo

if __name__ == "__main__":
    results = run_obloid_demo(
        mass_kg=2.7e23,
        r_m=1.0e-3,
        a_m=0.8e-3,
        A0_Bq=1.0e9,
        t12_days=30.0,
        total_yield=0.85,
        t_days=10.0,
        base_velocity=50.0,
        n_angles=361,
        inverse_time=False,
        save_prefix="salient",
        show=True
    )

    with open("obloid_angular_sweep.csv", "w", newline="") as f:
        w = csv.writer(f)
        w.writerow(["theta_deg", "alpha", "phase_corr_deg",
                    "velocity_uncorrected_um_s", "velocity_corrected_um_s",
                    "gamma_curies"])
        for i in range(len(results["theta_deg"])):
            w.writerow([
                results["theta_deg"][i],
                results["alpha"][i],
                results["phase_corr_deg"][i],
                results["v_uncorrected_um_s"][i],
                results["v_corrected_um_s"][i],
                results["gamma_curies"][i]
            ])

    print("Saved: salient_* plots and obloid_angular_sweep.csv")
PYCODE

# 4. Write ASCII diagram file (continued)
cat >> "$WORKDIR/obloid_ascii_diagram.txt" <<'TXT'
   │   │   Racetrack Edge (graphene/hBN or Si/SiGe 2DEG)                │
   │   │                                                                │
   │   │   ← Chiral Edge Direction (CW under +B)                        │
   │   │                                                                │
   │   │   ┌───────────── Pump / RF Section ──────────────┐              │
   │   │   │  G1 (0°)   G2 (120°)   G3 (240°)             │              │
   │   │   │  ┌───────┐  ┌───────┐  ┌───────┐             │              │
   │   │   │  │  G1   │  │  G2   │  │  G3   │             │              │
   │   │   │  └───────┘  └───────┘  └───────┘             │              │
   │   │   └──────────────────────────────────────────────┘              │
   │   │                                                                │
   │   │   [QPC1]                                           [QPC2]      │
   │   │                                                                │
   │   │            █████████    (Magnetic SLED zone)                   │
   │   │            █  SLED  █    Fe / FeCo, 200–300 nm                 │
   │   │            █████████                                        ▲  │
   │   │                                                                │
   │   │   Hall(0°)  Hall(60°) ... Hall(300°)                            │
   │   └──────────────────────────────────────────────────────────────┘
   │
   │  SAW IDT A (equator, along edge)     SAW IDT B (axis spur)
   └────────────────────────────────────────────────────────────────────┘

Side View (axis of obloid perpendicular to device plane)
--------------------------------------------------------

      z ↑ (Obloid symmetry axis)
        │
        │                SLED
        │               █████
        │   Spacer     ███████   (ALD Al2O3)
        │───────────────┄┄┄┄┄┄┄────────────── (device plane, equator: θ = 90°)
        │         2DEG / Graphene Edge
        │────────────── substrate ───────────
        │
        └────────────→ x (racetrack tangent)

Obloid Metric Proxy (not physical structure):
   α(θ) = sqrt(1 - r_s / sqrt(r^2 + a^2 cos^2 θ))
   Strongest redshift at θ = 90° (equator, device plane).
   Weaker along θ = 0° (axis), probed via axis spur + SAW IDT B.

RF Timing Overlay:
   G1: sin(ωt + φ1(φ))   G2: sin(ωt + φ2(φ))   G3: sin(ωt + φ3(φ))
   with φ-corrections chosen so local phase velocity ~ constant:
   dφ/ds ∝ 1/α(φ); cumulative correction wraps to 2π around track.
TXT

# 5. Create the tar.gz archive
tar -czf obloid_package.tar.gz "$WORKDIR"

# 6. Final message
echo "Archive created: obloid_package.tar.gz"
echo "Contains:"
echo " - obloid_sim_salient.py (simulation)"
echo " - export_salient.py (runner + CSV export)"
echo " - obloid_ascii_diagram.txt (functional ASCII schematic)"

 

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×.

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.
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Execution
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init_system()