// adi_hybrid_neuromorphic_internet4.0_system.cpp
// This program integrates components to create a hybrid system
// that leverages traditional HPC and neuromorphic computing.
// The primary goal is to demonstrate a workflow where a numerical task (gradient calculation)
// is handled by the HPC layer, and the resulting features are processed by a simulated
// Spiking Neural Network (SNN) for pattern recognition.
// Shouts out to the ASEF worldwide, let's reduce our pickaxe junking for Internet 4.0 so Natural Resource Scientists have less worries. How we do this? My initial first post which has been a point of interest to me allows classical computers to do this work though at a greater efficiency through distributed throughput than some packaging deal between assumed stock valuation sentiment of buyers and sellers. Lets hope our family estuaries are forward leveraged ahead of debenture capitalist cycles to debt risk indentitude of our common civilization.
// How do you do this? Call your insurance company, bank options management cycler to your home and of course expect your representative or constitutional governance bodies to respect the case of sovereignty or sovietry to provision our collective future saliently.
// --- Necessary Headers ---
#include <iostream>
#include <vector>
#include <string>
#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>
// For SIMD JSON parsing (assumes simdjson is available)
#include "simdjson.h"
using namespace simdjson;
// For HTTP server (assumes cpp-httplib is available)
#define CPPHTTPLIB_OPENSSL_SUPPORT
#include "httplib.h"
// For CUDA GPU support (assumes CUDA Toolkit and Thrust are installed)
#include <cuda_runtime.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/transform.h>
#include <thrust/adjacent_difference.h>
// --- Common Constants for Workflow Operations ---
// These constants define the types of operations that can be
// included in a workflow sent from a client.
const int OPERATION_CALCULATE_GRADIENT_1D = 2;
const int OPERATION_WORKFLOW = 6;
const int OPERATION_NEUROMORPHIC_PREDICT = 7;
// --- Helper Functions for CUDA and CPU support detection ---
// These functions check for the presence of the necessary hardware and
// runtime libraries.
bool has_cuda_support() {
int device_count = 0;
cudaError_t err = cudaGetDeviceCount(&device_count);
return err == cudaSuccess && device_count > 0;
}
// --- Neuromorphic Component: Spiking Neural Network ---
// Leaky Integrate-and-Fire (LIF) Neuron Model
// This class simulates a single LIF neuron. Its membrane potential
// integrates input current over time and "leaks" back to a resting potential.
// It fires a "spike" and resets when its potential exceeds a threshold.
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) {}
// Updates the neuron's state and returns true if it spiked.
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; // Spike
}
return false; // No spike
}
private:
double tau_m, v_rest, v_reset, v_thresh, membrane_potential;
};
// A simple Spiking Neural Network with two layers.
// This network processes an input vector by propagating spikes and
// counting the total spikes in the output layer.
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) {
// Initialize neurons
hidden_layer.resize(hidden_size);
output_layer.resize(output_size);
// Initialize random weights
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);
}
// Processes the input vector over a simulated period of time.
std::vector<int> predict(const std::vector<double>& input_vector, int num_timesteps = 100, double dt = 1.0) {
// Ensure input size matches the network's expected input.
if (input_vector.size() != input_size) {
throw std::runtime_error("Input vector size mismatch.");
}
std::vector<int> output_spike_counts(output_size, 0);
// Simulation loop over time steps
for (int t = 0; t < num_timesteps; ++t) {
std::vector<double> hidden_currents(hidden_size, 0.0);
// Calculate currents for the hidden layer
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);
// Update hidden neurons and propagate spikes
for (int j = 0; j < hidden_size; ++j) {
if (hidden_layer[j].update(hidden_currents[j], dt)) {
hidden_spikes[j] = true;
}
}
// Calculate currents for the output layer based on hidden spikes
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];
}
}
}
// Update output neurons and accumulate spike counts
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;
};
// --- Thrust Functor for Gradient Calculation ---
// This functor is used to compute the difference between adjacent elements on the GPU.
// It is specifically designed to work with Thrust's parallel algorithms.
struct gradient_functor {
template <typename T>
__host__ __device__ T operator()(const T& x, const T& y) const {
return y - x;
}
};
// --- Core Workflow Logic ---
std::string handle_workflow(const std::string& request_body) {
ondemand::parser parser;
padded_string json_data = padded_string::load(request_body);
ondemand::document doc = parser.iterate(json_data);
std::map<std::string, std::vector<double>> intermediate_results;
std::string response_str;
try {
for (auto element : doc["workflow"].get_array()) {
std::string op_type = std::string(element["operation_type"].get_string());
std::string output_id = std::string(element["output_id"].get_string());
std::vector<double> input_data_vec;
// Determine input source (direct data or intermediate result)
std::string input_type = std::string(element["input_data"]["type"].get_string());
if (input_type == "direct") {
for (auto val : element["input_data"]["data"].get_array()) {
input_data_vec.push_back(val.get_double());
}
} else if (input_type == "reference") {
std::string ref_id = std::string(element["input_data"]["reference_id"].get_string());
if (intermediate_results.count(ref_id)) {
input_data_vec = intermediate_results[ref_id];
} else {
throw std::runtime_error("Reference ID not found: " + ref_id);
}
}
// --- Execute Operation ---
if (op_type == "CALCULATE_GRADIENT_1D") {
std::vector<double> gradient;
if (input_data_vec.size() > 1) {
// Use GPU via Thrust if available, otherwise fall back to CPU
if (has_cuda_support()) {
std::cout << "Calculating gradient on GPU with Thrust." << std::endl;
thrust::host_vector<double> h_input = input_data_vec;
thrust::device_vector<double> d_input = h_input;
thrust::device_vector<double> d_gradient(d_input.size() - 1);
thrust::adjacent_difference(
d_input.begin(), d_input.end(), d_gradient.begin(), gradient_functor()
);
gradient = d_gradient; // Copy back to host
} else {
std::cout << "Calculating gradient on CPU with OpenMP." << std::endl;
gradient.resize(input_data_vec.size() - 1);
#pragma omp parallel for
for (size_t i = 0; i < input_data_vec.size() - 1; ++i) {
gradient[i] = input_data_vec[i+1] - input_data_vec[i];
}
}
}
intermediate_results[output_id] = gradient;
} else if (op_type == "NEUROMORPHIC_PREDICT") {
std::cout << "Processing data with the simulated SNN." << std::endl;
SpikingNetwork snn(input_data_vec.size(), 10, 5); // Example network
std::vector<int> spike_counts = snn.predict(input_data_vec);
// Build a JSON response with the spike counts
std::string spikes_json = "{ \"spike_counts\": [";
for (size_t i = 0; i < spike_counts.size(); ++i) {
spikes_json += std::to_string(spike_counts[i]);
if (i < spike_counts.size() - 1) {
spikes_json += ", ";
}
}
spikes_json += "] }";
response_str = spikes_json;
} else {
throw std::runtime_error("Unknown operation type: " + op_type);
}
}
} catch (const std::exception& e) {
return std::string("Error: ") + e.what();
}
return response_str;
}
// --- Server and Client Logic ---
void start_server() {
httplib::Server svr;
svr.Post("/workflow", [&](const httplib::Request& req, httplib::Response& res) {
try {
std::string response_str = handle_workflow(req.body);
res.set_content(response_str, "application/json");
res.status = 200;
} catch (const std::exception& e) {
res.set_content(e.what(), "text/plain");
res.status = 500;
}
});
std::cout << "Server listening on localhost:8080" << std::endl;
svr.listen("0.0.0.0", 8080);
}
void start_client() {
std::cout << "Client started. Sending workflow to server." << std::endl;
httplib::Client cli("localhost", 8080);
// This JSON defines a two-step workflow:
// 1. Calculate the gradient of a sample time-series signal.
// 2. Use that gradient as input for the neuromorphic SNN.
std::string workflow_json = R"({
"workflow": [
{
"operation_type": "CALCULATE_GRADIENT_1D",
"input_data": {
"type": "direct",
"data": [10.0, 11.5, 13.0, 12.0, 10.5, 9.0, 8.5]
},
"output_id": "gradient_result"
},
{
"operation_type": "NEUROMORPHIC_PREDICT",
"input_data": {
"type": "reference",
"reference_id": "gradient_result"
},
"output_id": "neuromorphic_result"
}
]
})";
if (auto res = cli.Post("/workflow", workflow_json, "application/json")) {
if (res->status == 200) {
std::cout << "Server response: " << res->body << std::endl;
} else {
std::cerr << "Server error: " << httplib::to_string(res.error()) << " Status: " << res->status << std::endl;
}
} else {
std::cerr << "Client error: " << httplib::to_string(res.error()) << std::endl;
}
}
int main() {
std::thread server_thread(start_server);
std::this_thread::sleep_for(std::chrono::seconds(1));
std::thread client_thread(start_client);
server_thread.join();
client_thread.join();
return 0;
}
--- VHDL Architecture for a Conceptual Neuromorphic Core
-- with an ARM-compatible interface and ONoC integration.
-- The ARM processor now communicates with the core via the ONoC,
-- treating it as a high-speed peripheral.
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 32-bit memory-mapped bus.
type arm_bus_master is record
addr_bus : std_logic_vector(31 downto 0);
write_data : std_logic_vector(31 downto 0);
read_data : std_logic_vector(31 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;
-- The top-level entity, now with both ARM and Optical interfaces.
-- The ARM bus would be used for configuration and control, while the
-- optical channels handle the high-speed data transfer.
entity NeuromorphicProcessor_ARM_ONoC is
port (
clk : in std_logic;
reset : in std_logic;
-- ARM Host Interface (memory-mapped) for control
arm_bus : inout arm_bus_master;
-- Optical Input Interface
optical_in_channel : in optical_channel;
-- Optical Output Interface
optical_out_channel : out optical_channel
);
end entity NeuromorphicProcessor_ARM_ONoC;
architecture Behavioral of NeuromorphicProcessor_ARM_ONoC is
-- Internal signals for the network state.
signal internal_data_bus : std_logic_vector(63 downto 0);
signal internal_spike_counts_bus : std_logic_vector(63 downto 0);
signal processing_done_signal : std_logic := '0';
-- Signals to interface with the ONoC component
signal onoc_electrical_in_bus : std_logic_vector(63 downto 0);
signal onoc_electrical_in_valid : std_logic := '0';
signal onoc_electrical_in_ready : std_logic;
signal onoc_electrical_out_bus : std_logic_vector(63 downto 0);
signal onoc_electrical_out_valid : std_logic;
signal onoc_electrical_out_ready : std_logic := '0';
-- Component for a single Leaky Integrate-and-Fire (LIF) neuron.
component LIFNeuron is
port (
clk, reset : in std_logic;
input_current : in std_logic_vector(63 downto 0);
spike_out : out std_logic;
membrane_potential : out std_logic_vector(63 downto 0)
);
end component;
-- Component for the Optical Network-on-Chip (ONoC) Interface.
component ONoC_Interface is
port (
clk, reset : in std_logic;
optical_in : in optical_channel;
optical_out: out optical_channel;
electrical_in_bus : in std_logic_vector(63 downto 0);
electrical_in_valid : in std_logic;
electrical_in_ready : out std_logic;
electrical_out_bus : out std_logic_vector(63 downto 0);
electrical_out_valid : out std_logic;
electrical_out_ready : in std_logic
);
end component;
-- FSM states for managing the process
type t_fsm_state is (S_IDLE, S_RUN_SIMULATION, S_OUTPUT_DATA);
signal fsm_state : t_fsm_state := S_IDLE;
-- Define the memory-mapped registers for ARM control
constant CONTROL_REG_ADDR : std_logic_vector(31 downto 0) := x"00000000";
constant STATUS_REG_ADDR : std_logic_vector(31 downto 0) := x"00000004";
begin
-- Instantiate the ONoC Interface to handle data movement.
ONoC_Inst : ONoC_Interface
port map (
clk => clk,
reset => reset,
optical_in => optical_in_channel,
optical_out=> optical_out_channel,
electrical_in_bus => internal_spike_counts_bus,
electrical_in_valid => processing_done_signal, -- Signals when results are ready
electrical_in_ready => onoc_electrical_in_ready,
electrical_out_bus => onoc_electrical_out_bus,
electrical_out_valid => onoc_electrical_out_valid,
electrical_out_ready => onoc_electrical_out_ready
);
-- Main Processing Logic
process (clk, reset)
begin
if reset = '1' then
fsm_state <= S_IDLE;
processing_done_signal <= '0';
onoc_electrical_out_ready <= '0';
elsif rising_edge(clk) then
case fsm_state is
when S_IDLE =>
onoc_electrical_out_ready <= '1'; -- Signal that we're ready for data from ONoC
if onoc_electrical_out_valid = '1' then
-- Data is available from the ONoC; load it for processing.
internal_data_bus <= onoc_electrical_out_bus;
fsm_state <= S_RUN_SIMULATION;
onoc_electrical_out_ready <= '0';
end if;
when S_RUN_SIMULATION =>
-- This is where the core neuromorphic logic would run.
-- The network would be controlled for a fixed number of timesteps.
-- After processing, prepare the output and transition.
-- This part is a placeholder for the actual neuron instantiation and logic.
-- Assume results are ready and stored in internal_spike_counts_bus
processing_done_signal <= '1';
fsm_state <= S_OUTPUT_DATA;
when S_OUTPUT_DATA =>
-- Check if the ONoC has acknowledged our output data.
if onoc_electrical_in_ready = '1' then
processing_done_signal <= '0';
fsm_state <= S_IDLE; -- Return to idle to await new input
end if;
end case;
end if;
end process;
-- ARM Bus Interface Logic
-- This now handles high-level control and status, not direct data transfer.
process (clk, reset)
begin
if reset = '1' then
-- Reset ARM interface
elsif rising_edge(clk) then
if arm_bus.write_en = '1' then
if arm_bus.addr_bus = CONTROL_REG_ADDR then
-- ARM writes a command (e.g., start, reset).
-- This would interact with the main FSM, if needed.
end if;
end if;
if arm_bus.read_en = '1' then
if arm_bus.addr_bus = STATUS_REG_ADDR then
-- ARM reads the status, e.g., if the core is idle.
arm_bus.read_data <= (others => '0'); -- Placeholder status
end if;
end if;
end if;
end process;
end architecture Behavioral;
# adi_hybrid_neuromorphic_suite.py
# This script is a Python translation and refit of the logic from the provided
# C and C++ files. It creates a hybrid computing system with a Flask web server,
# a JSON-based workflow engine, and a neuromorphic Spiking Neural Network (SNN).
# It leverages NumPy for high-performance CPU computation and can optionally use
# CuPy for GPU acceleration if a compatible NVIDIA GPU and CUDA are available.
import numpy as np
import json
from flask import Flask, request, jsonify
import requests
import threading
import time
# --- GPU Support Check ---
# This section checks for the availability of CuPy, which is used for GPU acceleration.
# If CuPy is not found, the application will gracefully fall back to using NumPy on the CPU.
try:
import cupy as cp
CUPY_AVAILABLE = True
print("CuPy found. GPU acceleration is enabled.")
except ImportError:
CUPY_AVAILABLE = False
print("CuPy not found. Using NumPy for CPU computation.")
# --- Neuromorphic Component: Spiking Neural Network (SNN) ---
class LIFNeuron:
"""
A Python implementation of the Leaky Integrate-and-Fire (LIF) neuron model.
This class simulates the behavior of a single biological neuron.
"""
def __init__(self, tau_m=20.0, v_rest=-65.0, v_reset=-65.0, v_thresh=-50.0):
self.tau_m = tau_m # Membrane time constant
self.v_rest = v_rest # Resting potential
self.v_reset = v_reset # Reset potential after a spike
self.v_thresh = v_thresh # Firing threshold
self.membrane_potential = v_rest
def update(self, input_current, dt=1.0):
"""
Updates the neuron's membrane potential based on input current.
Returns True if the neuron fires a spike, otherwise False.
"""
dv = (-(self.membrane_potential - self.v_rest) + input_current) / self.tau_m
self.membrane_potential += dv * dt
if self.membrane_potential >= self.v_thresh:
self.membrane_potential = self.v_reset
return True # Spike occurred
return False # No spike
class SpikingNetwork:
"""
A simple two-layer Spiking Neural Network (SNN).
This network processes input data by simulating the firing of interconnected LIF neurons.
"""
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# Initialize neurons for hidden and output layers
self.hidden_layer = [LIFNeuron() for _ in range(hidden_size)]
self.output_layer = [LIFNeuron() for _ in range(output_size)]
# Initialize weights with random values using NumPy for efficiency
self.input_to_hidden_weights = np.random.rand(input_size, hidden_size)
self.hidden_to_output_weights = np.random.rand(hidden_size, output_size)
def predict(self, input_vector, num_timesteps=100, dt=1.0):
"""
Processes an input vector over a series of time steps and returns the
total number of spikes from the output neurons.
"""
if len(input_vector) != self.input_size:
raise ValueError("Input vector size does not match network input size.")
output_spike_counts = np.zeros(self.output_size, dtype=int)
# The core simulation loop
for _ in range(num_timesteps):
# Calculate input currents for the hidden layer using matrix multiplication
hidden_currents = np.dot(input_vector, self.input_to_hidden_weights)
# Update hidden neurons and record spikes
hidden_spikes = np.array([neuron.update(current, dt) for neuron, current in zip(self.hidden_layer, hidden_currents)])
# Calculate input currents for the output layer if any hidden neurons spiked
if np.any(hidden_spikes):
output_currents = np.dot(hidden_spikes, self.hidden_to_output_weights)
else:
output_currents = np.zeros(self.output_size)
# Update output neurons and count spikes
for k, current in enumerate(output_currents):
if self.output_layer[k].update(current, dt):
output_spike_counts[k] += 1
return output_spike_counts.tolist()
# --- High-Performance Computing Functions ---
def calculate_gradient_1d(data):
"""
Calculates the 1D gradient (difference between adjacent elements) of an array.
It automatically uses CuPy for GPU acceleration if available, otherwise NumPy.
"""
if not isinstance(data, (np.ndarray, list)):
raise TypeError("Input data must be a list or NumPy array.")
if CUPY_AVAILABLE:
# Use GPU
print("Calculating gradient on GPU with CuPy.")
gpu_array = cp.asarray(data)
gradient = cp.diff(gpu_array)
return cp.asnumpy(gradient).tolist() # Return result as a standard Python list
else:
# Use CPU
print("Calculating gradient on CPU with NumPy.")
cpu_array = np.asarray(data)
gradient = np.diff(cpu_array)
return gradient.tolist()
# --- Core Workflow and Server Logic ---
app = Flask(__name__)
intermediate_results = {}
@app.route('/workflow', methods=['POST'])
def handle_workflow_request():
"""
Handles incoming JSON workflow requests. This function parses the workflow,
executes the specified operations in sequence, and returns the final result.
"""
try:
workflow = request.json['workflow']
final_result = None
for step in workflow:
op_type = step['operation_type']
input_data_def = step['input_data']
output_id = step['output_id']
# Resolve input data (either direct or from a previous step)
if input_data_def['type'] == 'direct':
input_data = input_data_def['data']
elif input_data_def['type'] == 'reference':
ref_id = input_data_def['reference_id']
if ref_id in intermediate_results:
input_data = intermediate_results[ref_id]
else:
return jsonify({"error": f"Reference ID not found: {ref_id}"}), 400
# Execute the requested operation
if op_type == 'CALCULATE_GRADIENT_1D':
result = calculate_gradient_1d(input_data)
intermediate_results[output_id] = result
final_result = result
elif op_type == 'NEUROMORPHIC_PREDICT':
# Initialize the SNN with the correct input size
snn = SpikingNetwork(input_size=len(input_data), hidden_size=20, output_size=5)
result = snn.predict(input_data)
intermediate_results[output_id] = result
final_result = {"spike_counts": result}
else:
return jsonify({"error": f"Unknown operation type: {op_type}"}), 400
return jsonify(final_result)
except Exception as e:
return jsonify({"error": str(e)}), 500
def start_server():
"""Starts the Flask web server."""
# Running in debug mode is not recommended for production
app.run(host='0.0.0.0', port=8080, debug=False)
def start_client():
"""
A simple client to demonstrate sending a workflow request to the server.
"""
print("\nClient started. Sending workflow to the server...")
# This JSON defines the same two-step workflow as the C++ example:
# 1. Calculate the gradient of a time-series signal.
# 2. Feed the gradient into the SNN for prediction.
workflow_json = {
"workflow": [
{
"operation_type": "CALCULATE_GRADIENT_1D",
"input_data": {
"type": "direct",
"data": [10.0, 11.5, 13.0, 12.0, 10.5, 9.0, 8.5]
},
"output_id": "gradient_result"
},
{
"operation_type": "NEUROMORPHIC_PREDICT",
"input_data": {
"type": "reference",
"reference_id": "gradient_result"
},
"output_id": "neuromorphic_result"
}
]
}
try:
response = requests.post('http://localhost:8080/workflow', json=workflow_json)
response.raise_for_status() # Raise an exception for bad status codes
print("Server Response:")
print(response.json())
except requests.exceptions.RequestException as e:
print(f"Client Error: Could not connect to the server. {e}")
if __name__ == '__main__':
# Start the server in a separate thread so the client can run
server_thread = threading.Thread(target=start_server)
server_thread.daemon = True
server_thread.start()
# Give the server a moment to start up
time.sleep(2)
# Run the client
start_client()
# Keep the main thread alive to allow the server to run
# In a real application, you might have a more robust shutdown mechanism
server_thread.join()
# --- Extended Use Case System and Examples ---
#
# The following sections are commented-out conceptual examples of how this
# hybrid computing suite could be extended for other applications.
#
# ==============================================================================
# --- USE CASE 1: REAL-TIME ANOMALY DETECTION IN FINANCIAL DATA ---
# ==============================================================================
#
# CONCEPT:
# A financial institution wants to detect anomalous trading patterns in real-time.
# High-frequency trading data (e.g., stock prices, volumes) streams into the system.
# The system must preprocess this data and use the neuromorphic SNN to spot
# patterns that deviate from the norm, potentially indicating market manipulation
# or a system glitch.
#
# WORKFLOW:
# 1. Data Ingestion: A separate process (e.g., a Kafka consumer) receives raw trade data.
# 2. Preprocessing (HPC): The raw data is batched into time windows (e.g., 1-second intervals).
# The `CALCULATE_GRADIENT_1D` operation is used on the price data to determine the
# rate of change (velocity) and acceleration, which are key features. This happens on the GPU.
# 3. Anomaly Detection (Neuromorphic): The calculated gradients (features) are fed into
# a pre-trained Spiking Neural Network. The SNN is trained to recognize "normal"
# market behavior. If an input pattern results in an unusual spike count from the
# output neurons (e.g., a neuron designated for "high volatility" fires excessively),
# an alert is triggered.
#
#
# @app.route('/financial_anomaly', methods=['POST'])
# def handle_financial_data():
# """
# A conceptual endpoint for handling a stream of financial data.
# """
# trade_data = request.json.get('trades', [])
# if not trade_data:
# return jsonify({"error": "No trade data provided"}), 400
#
# # In a real system, this would be a more complex workflow submission
# # to the existing '/workflow' endpoint.
# prices = [trade['price'] for trade in trade_data]
#
# # 1. Preprocessing: Calculate price velocity
# price_velocity = calculate_gradient_1d(prices)
#
# # 2. Neuromorphic Prediction
# # Assume an SNN is trained for this specific task
# snn_input_size = len(price_velocity)
# anomaly_snn = SpikingNetwork(input_size=snn_input_size, hidden_size=50, output_size=3)
# # Output neurons could represent: [normal, moderate_volatility, high_anomaly]
# spike_counts = anomaly_snn.predict(price_velocity)
#
# # 3. Decision Logic
# if spike_counts[2] > 10: # Threshold for the "high_anomaly" neuron
# alert_message = f"High anomaly detected! Spike count: {spike_counts[2]}"
# print(alert_message)
# return jsonify({"status": "ALERT", "message": alert_message})
# else:
# return jsonify({"status": "OK", "spike_counts": spike_counts})
#
# ==============================================================================
# --- USE CASE 2: GAME ENGINE NETWORK THROUGHPUT & WEBCASTING ---
# ==============================================================================
#
# CONCEPT:
# This server can be extended to act as a simple backend for a multiplayer game
# or a live webcasting service.
#
# --- Part A: Game Engine State Synchronization ---
#
# A simple game where players control positions. The server receives updates
# from clients and broadcasts the new game state to all connected clients.
# The SNN could be used here for bot AI, predicting player movement.
#
# from flask_socketio import SocketIO, emit
#
# # This would require installing flask_socketio: pip install flask-socketio
# socketio = SocketIO(app)
# game_state = {'players': {}} # Store player positions
#
# @socketio.on('connect')
# def handle_connect():
# print('Client connected')
#
# @socketio.on('disconnect')
# def handle_disconnect():
# print('Client disconnected')
#
# @socketio.on('player_update')
# def handle_player_update(data):
# """
# Receives a position update from a player and broadcasts it.
# 'data' would be a JSON like: {'player_id': 'some_id', 'position': [x, y, z]}
# """
# player_id = data.get('player_id')
# position = data.get('position')
# if player_id and position:
# game_state['players'][player_id] = position
# # Broadcast the new state to all clients
# emit('game_state_update', game_state, broadcast=True)
#
# --- Part B: Simple Webcasting and HTML Form Posting ---
#
# The server can serve a simple HTML page and handle form submissions.
# It can also use WebSockets to push live updates to the web page.
#
# from flask import render_template_string
#
# HTML_TEMPLATE = """
# <!DOCTYPE html>
# <html>
# <head>
# <title>Hybrid Compute Interface</title>
# <script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"></script>
# </head>
# <body>
# <h1>Post a Message</h1>
# <form action="/post_message" method="post">
# <input type="text" name="message" placeholder="Enter message">
# <button type="submit">Post</button>
# </form>
# <h2>Live Webcast:</h2>
# <div id="webcast"></div>
#
# <script>
# var socket = io.connect('http://' + document.domain + ':' + location.port);
# socket.on('new_message', function(data) {
# var p = document.createElement('p');
# p.innerHTML = data.message;
# document.getElementById('webcast').appendChild(p);
# });
# </script>
# </body>
# </html>
# """
#
# @app.route('/')
# def index():
# """Serves the main HTML page."""
# return render_template_string(HTML_TEMPLATE)
#
# @app.route('/post_message', methods=['POST'])
# def post_message():
# """Handles form submission and webcasts the message."""
# message = request.form.get('message', 'empty message')
# # Use the socketio instance from Part A to broadcast
# socketio.emit('new_message', {'message': message})
# return 'Message posted and broadcasted!'
#
#
# To run the full example with WebSockets, you would need to modify the
# main execution block:
#
# if __name__ == '__main__':
# # The server would be started with socketio.run() instead of app.run()
# # socketio.run(app, host='0.0.0.0', port=8080)
# pass
No comments:
Post a Comment