Neuromorphic Computing Systems

# Neuromorphic Computing System Implementation
import numpy as np
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
import asyncio
from concurrent.futures import ThreadPoolExecutor

@dataclass
class SpikingNeuron:
    neuron_id: int
    membrane_potential: float
    threshold: float
    refractory_period: int
    last_spike_time: int
    input_synapses: List['Synapse']
    output_synapses: List['Synapse']

@dataclass 
class Synapse:
    pre_neuron_id: int
    post_neuron_id: int
    weight: float
    delay: int
    plasticity_rule: str
    last_update: int

@dataclass
class SpikeEvent:
    neuron_id: int
    timestamp: int
    amplitude: float

class NeuromorphicProcessor:
    def __init__(self, config: Dict):
        self.neurons: Dict[int, SpikingNeuron] = {}
        self.synapses: Dict[Tuple[int, int], Synapse] = {}
        self.spike_queue: List[SpikeEvent] = []
        self.current_time = 0
        self.learning_rate = config.get('learning_rate', 0.001)
        self.global_inhibition = config.get('global_inhibition', False)
        
        # Performance monitoring
        self.spike_count = 0
        self.energy_consumption = 0.0
        
    def create_neuron(
        self, 
        neuron_id: int, 
        threshold: float = 1.0,
        refractory_period: int = 2
    ) -> SpikingNeuron:
        """Create a new spiking neuron"""
        neuron = SpikingNeuron(
            neuron_id=neuron_id,
            membrane_potential=0.0,
            threshold=threshold,
            refractory_period=refractory_period,
            last_spike_time=-refractory_period,
            input_synapses=[],
            output_synapses=[]
        )
        
        self.neurons[neuron_id] = neuron
        return neuron
    
    def create_synapse(
        self,
        pre_neuron_id: int,
        post_neuron_id: int,
        initial_weight: float = 0.5,
        delay: int = 1,
        plasticity_rule: str = 'STDP'
    ) -> Synapse:
        """Create synaptic connection between neurons"""
        
        if pre_neuron_id not in self.neurons or post_neuron_id not in self.neurons:
            raise ValueError("Both neurons must exist before creating synapse")
        
        synapse = Synapse(
            pre_neuron_id=pre_neuron_id,
            post_neuron_id=post_neuron_id,
            weight=initial_weight,
            delay=delay,
            plasticity_rule=plasticity_rule,
            last_update=self.current_time
        )
        
        # Add to neuron connection lists
        self.neurons[pre_neuron_id].output_synapses.append(synapse)
        self.neurons[post_neuron_id].input_synapses.append(synapse)
        
        # Store in synapse dictionary
        self.synapses[(pre_neuron_id, post_neuron_id)] = synapse
        
        return synapse
    
    async def process_timestep(self) -> List[SpikeEvent]:
        """Process one simulation timestep"""
        
        current_spikes = []
        
        # Process all neurons in parallel for performance
        neuron_updates = await self.parallel_neuron_update()
        
        # Generate spikes for neurons above threshold
        for neuron_id, (membrane_potential, should_spike) in neuron_updates.items():
            neuron = self.neurons[neuron_id]
            neuron.membrane_potential = membrane_potential
            
            if should_spike and self.is_neuron_ready(neuron):
                spike = self.generate_spike(neuron)
                current_spikes.append(spike)
                
                # Reset neuron after spike
                neuron.membrane_potential = 0.0
                neuron.last_spike_time = self.current_time
        
        # Process synaptic transmission with delays
        await self.process_spike_propagation(current_spikes)
        
        # Update synaptic weights based on spike-timing
        await self.update_synaptic_plasticity(current_spikes)
        
        # Update global state
        self.current_time += 1
        self.spike_count += len(current_spikes)
        
        return current_spikes
    
    async def parallel_neuron_update(self) -> Dict[int, Tuple[float, bool]]:
        """Update all neurons in parallel"""
        
        update_tasks = []
        for neuron_id, neuron in self.neurons.items():
            task = asyncio.create_task(
                self.update_single_neuron(neuron)
            )
            update_tasks.append((neuron_id, task))
        
        # Wait for all updates
        results = {}
        for neuron_id, task in update_tasks:
            membrane_potential, should_spike = await task
            results[neuron_id] = (membrane_potential, should_spike)
        
        return results
    
    async def update_single_neuron(
        self, 
        neuron: SpikingNeuron
    ) -> Tuple[float, bool]:
        """Update a single neuron's state"""
        
        # Calculate input current from synapses
        input_current = 0.0
        for synapse in neuron.input_synapses:
            # Check for delayed spikes
            delayed_spikes = self.get_delayed_spikes(
                synapse.pre_neuron_id,
                synapse.delay
            )
            
            for spike in delayed_spikes:
                input_current += synapse.weight * spike.amplitude
        
        # Leaky integrate-and-fire dynamics
        decay_factor = 0.95  # Membrane leak
        neuron.membrane_potential = (
            neuron.membrane_potential * decay_factor + 
            input_current
        )
        
        # Add noise for biological realism
        noise = np.random.normal(0, 0.01)
        neuron.membrane_potential += noise
        
        # Check for spike condition
        should_spike = (
            neuron.membrane_potential >= neuron.threshold and
            self.is_neuron_ready(neuron)
        )
        
        # Energy consumption calculation
        self.energy_consumption += 0.1 + abs(input_current) * 0.05
        
        return neuron.membrane_potential, should_spike
    
    def is_neuron_ready(self, neuron: SpikingNeuron) -> bool:
        """Check if neuron is out of refractory period"""
        return (self.current_time - neuron.last_spike_time) >= neuron.refractory_period
    
    def generate_spike(self, neuron: SpikingNeuron) -> SpikeEvent:
        """Generate spike event for neuron"""
        return SpikeEvent(
            neuron_id=neuron.neuron_id,
            timestamp=self.current_time,
            amplitude=1.0
        )
    
    def get_delayed_spikes(self, neuron_id: int, delay: int) -> List[SpikeEvent]:
        """Get spikes that should arrive now due to synaptic delay"""
        target_time = self.current_time - delay
        
        return [
            spike for spike in self.spike_queue
            if spike.neuron_id == neuron_id and spike.timestamp == target_time
        ]
    
    async def process_spike_propagation(self, current_spikes: List[SpikeEvent]):
        """Handle spike propagation through network"""
        
        # Add spikes to queue for future delivery
        for spike in current_spikes:
            self.spike_queue.append(spike)
        
        # Remove old spikes to prevent memory buildup
        max_delay = max((s.delay for s in self.synapses.values()), default=1)
        cutoff_time = self.current_time - max_delay - 10
        
        self.spike_queue = [
            spike for spike in self.spike_queue
            if spike.timestamp > cutoff_time
        ]
    
    async def update_synaptic_plasticity(self, current_spikes: List[SpikeEvent]):
        """Update synaptic weights based on spike timing"""
        
        for spike in current_spikes:
            neuron = self.neurons[spike.neuron_id]
            
            # Update all input synapses (STDP)
            for synapse in neuron.input_synapses:
                if synapse.plasticity_rule == 'STDP':
                    await self.apply_stdp(synapse, spike)
    
    async def apply_stdp(self, synapse: Synapse, post_spike: SpikeEvent):
        """Apply Spike-Timing-Dependent Plasticity"""
        
        # Find recent pre-synaptic spikes
        pre_spikes = [
            spike for spike in self.spike_queue
            if (spike.neuron_id == synapse.pre_neuron_id and
                abs(spike.timestamp - post_spike.timestamp) <= 20)
        ]
        
        for pre_spike in pre_spikes:
            # Calculate time difference
            dt = post_spike.timestamp - pre_spike.timestamp
            
            if dt > 0:  # Post before pre (LTD)
                weight_change = -self.learning_rate * np.exp(-dt / 20)
            else:  # Pre before post (LTP)
                weight_change = self.learning_rate * np.exp(dt / 20)
            
            # Update weight with bounds
            synapse.weight = np.clip(
                synapse.weight + weight_change,
                0.0,  # Minimum weight
                2.0   # Maximum weight
            )
            
            synapse.last_update = self.current_time
    
    def get_network_statistics(self) -> Dict:
        """Get comprehensive network performance statistics"""
        
        total_neurons = len(self.neurons)
        total_synapses = len(self.synapses)
        
        # Calculate average membrane potentials
        avg_membrane_potential = np.mean([
            neuron.membrane_potential 
            for neuron in self.neurons.values()
        ])
        
        # Calculate average synaptic weights
        avg_weight = np.mean([
            synapse.weight 
            for synapse in self.synapses.values()
        ])
        
        # Calculate spike rate
        spike_rate = self.spike_count / max(self.current_time, 1) / total_neurons
        
        # Calculate energy efficiency
        energy_per_spike = self.energy_consumption / max(self.spike_count, 1)
        
        return {
            'neurons': total_neurons,
            'synapses': total_synapses,
            'simulation_time': self.current_time,
            'total_spikes': self.spike_count,
            'spike_rate_hz': spike_rate * 1000,  # Assuming 1ms timesteps
            'avg_membrane_potential': avg_membrane_potential,
            'avg_synaptic_weight': avg_weight,
            'energy_consumption_mj': self.energy_consumption,
            'energy_per_spike_pj': energy_per_spike * 1e6,
            'network_activity': min(spike_rate * 100, 100)
        }

# High-level Neuromorphic Computing System
class NeuromorphicComputingSystem:
    def __init__(self, config: Dict):
        self.processor = NeuromorphicProcessor(config)
        self.input_encoders = {}
        self.output_decoders = {}
        self.learning_algorithms = {}
        
    async def create_network_topology(
        self, 
        layers: List[int],
        connectivity: str = 'fully_connected'
    ):
        """Create multi-layer network topology"""
        
        layer_neurons = []
        
        # Create neurons for each layer
        for layer_idx, num_neurons in enumerate(layers):
            layer = []
            for neuron_idx in range(num_neurons):
                neuron_id = layer_idx * 10000 + neuron_idx
                neuron = self.processor.create_neuron(neuron_id)
                layer.append(neuron)
            layer_neurons.append(layer)
        
        # Create connections between layers
        if connectivity == 'fully_connected':
            await self.create_fully_connected_layers(layer_neurons)
        elif connectivity == 'convolutional':
            await self.create_convolutional_layers(layer_neurons)
        
        return layer_neurons
    
    async def train_on_spike_patterns(
        self,
        input_patterns: List[List[int]],
        target_patterns: List[List[int]],
        epochs: int = 100
    ):
        """Train network on spike pattern datasets"""
        
        for epoch in range(epochs):
            epoch_error = 0.0
            
            for input_pattern, target_pattern in zip(input_patterns, target_patterns):
                # Encode input as spike train
                await self.encode_input_spikes(input_pattern)
                
                # Run forward pass
                output_spikes = []
                for timestep in range(100):  # 100ms simulation
                    spikes = await self.processor.process_timestep()
                    output_spikes.extend(spikes)
                
                # Calculate error and backpropagate (simplified)
                error = self.calculate_spike_pattern_error(
                    output_spikes, 
                    target_pattern
                )
                epoch_error += error
                
                # Adjust learning based on error
                await self.adjust_learning_rate(error)
            
            # Log training progress
            if epoch % 10 == 0:
                stats = self.processor.get_network_statistics()
                print(f"Epoch {epoch}: Error={epoch_error:.4f}, "
                      f"Energy={stats['energy_per_spike_pj']:.2f}pJ/spike")
    
    async def encode_input_spikes(self, input_pattern: List[int]):
        """Convert input data to spike trains"""
        
        for neuron_idx, intensity in enumerate(input_pattern):
            if intensity > 0:
                # Generate spikes proportional to intensity
                for _ in range(int(intensity * 10)):
                    await asyncio.sleep(0.001)  # Spike timing
                    spike = SpikeEvent(
                        neuron_id=neuron_idx,
                        timestamp=self.processor.current_time,
                        amplitude=intensity / 255.0
                    )
                    self.processor.spike_queue.append(spike)

// Memristive Synapse Management System
#include <vector>
#include <memory>
#include <atomic>
#include <chrono>
#include <map>

class MemristiveSynapse {
private:
    double resistance;        // Current resistance state
    double min_resistance;   // Minimum resistance (LRS)
    double max_resistance;   // Maximum resistance (HRS) 
    double voltage_threshold; // Switching threshold
    std::atomic<double> weight; // Synaptic weight
    
    // Learning parameters
    double learning_rate;
    double decay_factor;
    std::chrono::high_resolution_clock::time_point last_update;
    
public:
    MemristiveSynapse(double min_r = 100.0, double max_r = 10000.0)
        : min_resistance(min_r), max_resistance(max_r),
          voltage_threshold(0.5), learning_rate(0.01), decay_factor(0.99) {
        
        resistance = (min_resistance + max_resistance) / 2.0;
        weight = resistance_to_weight(resistance);
        last_update = std::chrono::high_resolution_clock::now();
    }
    
    // Update resistance based on applied voltage
    void apply_voltage(double voltage, double duration_ms) {
        auto current_time = std::chrono::high_resolution_clock::now();
        auto time_diff = std::chrono::duration_cast<std::chrono::milliseconds>
                        (current_time - last_update).count();
        
        // Apply voltage-driven resistance change
        if (std::abs(voltage) > voltage_threshold) {
            double resistance_change = calculate_resistance_change(
                voltage, duration_ms
            );
            
            // Update resistance with bounds
            resistance = std::clamp(
                resistance + resistance_change,
                min_resistance,
                max_resistance
            );
            
            // Convert to synaptic weight
            weight = resistance_to_weight(resistance);
        }
        
        // Apply temporal decay
        if (time_diff > 100) { // 100ms decay interval
            apply_temporal_decay();
        }
        
        last_update = current_time;
    }
    
    // Spike-timing-dependent plasticity update
    void update_stdp(double pre_spike_time, double post_spike_time) {
        double dt = post_spike_time - pre_spike_time; // milliseconds
        double weight_change = 0.0;
        
        if (dt > 0 && dt < 50) { // LTP window
            weight_change = learning_rate * std::exp(-dt / 20.0);
        } else if (dt < 0 && dt > -50) { // LTD window  
            weight_change = -learning_rate * 0.5 * std::exp(dt / 20.0);
        }
        
        // Convert weight change to resistance change
        double new_weight = std::clamp(
            weight.load() + weight_change,
            0.0, 1.0
        );
        
        resistance = weight_to_resistance(new_weight);
        weight = new_weight;
    }
    
    double get_current_weight() const {
        return weight.load();
    }
    
    double get_power_consumption() const {
        // Power consumption based on resistance and activity
        return 0.1 / resistance; // Inverse relationship
    }
    
private:
    double calculate_resistance_change(double voltage, double duration) {
        // Simplified memristor model
        double magnitude = std::abs(voltage);
        double polarity = (voltage > 0) ? -1.0 : 1.0; // Negative voltage increases R
        
        return polarity * magnitude * duration * 0.01;
    }
    
    double resistance_to_weight(double r) {
        // Convert resistance to synaptic weight (0-1 range)
        return (max_resistance - r) / (max_resistance - min_resistance);
    }
    
    double weight_to_resistance(double w) {
        // Convert weight to resistance
        return max_resistance - w * (max_resistance - min_resistance);
    }
    
    void apply_temporal_decay() {
        // Resistance drift toward middle state
        double target_resistance = (min_resistance + max_resistance) / 2.0;
        resistance += (target_resistance - resistance) * (1.0 - decay_factor);
        weight = resistance_to_weight(resistance);
    }
};

class NeuromorphicChipController {
private:
    std::vector<std::vector<std::unique_ptr<MemristiveSynapse>>> synapse_matrix;
    std::map<int, double> neuron_states;
    std::atomic<double> total_power_consumption{0.0};
    
    // Hardware interface
    struct HardwareInterface {
        void write_synapse_voltage(int row, int col, double voltage);
        double read_synapse_resistance(int row, int col);
        void set_neuron_threshold(int neuron_id, double threshold);
        double get_neuron_membrane_potential(int neuron_id);
    } hw_interface;
    
public:
    NeuromorphicChipController(int rows, int cols) {
        // Initialize synapse matrix
        synapse_matrix.resize(rows);
        for (int i = 0; i < rows; ++i) {
            synapse_matrix[i].resize(cols);
            for (int j = 0; j < cols; ++j) {
                synapse_matrix[i][j] = std::make_unique<MemristiveSynapse>();
            }
        }
    }
    
    // Process spike event and update synapses
    void process_spike(int pre_neuron, int post_neuron, double spike_time) {
        if (pre_neuron < synapse_matrix.size() && 
            post_neuron < synapse_matrix[0].size()) {
            
            auto& synapse = synapse_matrix[pre_neuron][post_neuron];
            
            // Apply STDP update
            double post_spike_time = get_last_spike_time(post_neuron);
            synapse->update_stdp(spike_time, post_spike_time);
            
            // Update hardware state
            double new_weight = synapse->get_current_weight();
            hw_interface.write_synapse_voltage(
                pre_neuron, post_neuron,
                weight_to_voltage(new_weight)
            );
            
            // Update power tracking
            total_power_consumption += synapse->get_power_consumption();
        }
    }
    
    // Batch update for efficiency
    void batch_update_synapses(
        const std::vector<std::tuple<int, int, double>>& spike_events
    ) {
        #pragma omp parallel for
        for (const auto& event : spike_events) {
            int pre = std::get<0>(event);
            int post = std::get<1>(event);
            double time = std::get<2>(event);
            
            process_spike(pre, post, time);
        }
        
        // Update global power consumption
        update_power_statistics();
    }
    
    // Get network performance metrics
    struct NetworkMetrics {
        double average_weight;
        double weight_variance;
        double power_consumption_mw;
        double learning_activity;
        int active_synapses;
    };
    
    NetworkMetrics get_network_metrics() const {
        NetworkMetrics metrics{};
        double weight_sum = 0.0;
        double weight_sq_sum = 0.0;
        int active_count = 0;
        
        for (const auto& row : synapse_matrix) {
            for (const auto& synapse : row) {
                double weight = synapse->get_current_weight();
                
                if (weight > 0.01) { // Active threshold
                    weight_sum += weight;
                    weight_sq_sum += weight * weight;
                    active_count++;
                }
            }
        }
        
        if (active_count > 0) {
            metrics.average_weight = weight_sum / active_count;
            metrics.weight_variance = (weight_sq_sum / active_count) - 
                                    (metrics.average_weight * metrics.average_weight);
        }
        
        metrics.power_consumption_mw = total_power_consumption.load();
        metrics.active_synapses = active_count;
        metrics.learning_activity = calculate_learning_activity();
        
        return metrics;
    }
    
    // Calibrate hardware parameters
    void calibrate_chip() {
        // Test each synapse and adjust parameters
        for (int i = 0; i < synapse_matrix.size(); ++i) {
            for (int j = 0; j < synapse_matrix[i].size(); ++j) {
                calibrate_single_synapse(i, j);
            }
        }
    }
    
private:
    double get_last_spike_time(int neuron_id) {
        auto it = neuron_states.find(neuron_id);
        return (it != neuron_states.end()) ? it->second : 0.0;
    }
    
    double weight_to_voltage(double weight) {
        // Convert normalized weight to hardware voltage
        return weight * 2.0 - 1.0; // Map [0,1] to [-1,1]V
    }
    
    void update_power_statistics() {
        // Calculate instantaneous power consumption
        double power = 0.0;
        for (const auto& row : synapse_matrix) {
            for (const auto& synapse : row) {
                power += synapse->get_power_consumption();
            }
        }
        total_power_consumption = power;
    }
    
    double calculate_learning_activity() {
        // Measure recent learning activity
        return total_power_consumption.load() / synapse_matrix.size() 
               / synapse_matrix[0].size();
    }
    
    void calibrate_single_synapse(int row, int col) {
        // Hardware-specific calibration procedure
        auto& synapse = synapse_matrix[row][col];
        
        // Test resistance range
        double test_voltage = 1.0;
        synapse->apply_voltage(test_voltage, 10.0); // 10ms pulse
        
        // Read hardware response
        double measured_resistance = hw_interface.read_synapse_resistance(row, col);
        
        // Adjust parameters based on measurement
        // (Hardware-specific calibration logic)
    }
};