Neuromorphic Computing

import numpy as np
import asyncio
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
from enum import Enum
 
class NeuronModel(Enum):
    LEAKY_INTEGRATE_FIRE = "lif"
    ADAPTIVE_EXPONENTIAL = "adex"
    IZHIKEVICH = "izh"
    HODGKIN_HUXLEY = "hh"
 
@dataclass
class SpikeEvent:
    timestamp: float
    neuron_id: int
    layer_id: int
    spike_amplitude: float = 1.0
 
class NeuromorphicProcessor:
    def __init__(self, architecture_config: Dict):
        self.config = architecture_config
        self.layers = self.initialize_neural_layers()
        self.synaptic_matrix = self.initialize_synaptic_connections()
        self.event_queue = asyncio.Queue()
        self.membrane_potentials = {}
        self.spike_history = []
        self.learning_rules = self.configure_learning_rules()
 
    def initialize_neural_layers(self) -> Dict[int, Dict]:
        """Initialize neuromorphic layers with specified neuron models"""
        layers = {}
 
        for layer_id, layer_config in self.config['layers'].items():
            layers[layer_id] = {
                'neuron_count': layer_config['size'],
                'neuron_model': NeuronModel(layer_config['model']),
                'neuron_parameters': self.get_neuron_parameters(layer_config['model']),
                'membrane_potentials': np.zeros(layer_config['size']),
                'refractory_timers': np.zeros(layer_config['size']),
                'adaptation_variables': np.zeros(layer_config['size'])
            }
 
        return layers
 
    def get_neuron_parameters(self, model_type: str) -> Dict:
        """Get model-specific neuron parameters"""
        if model_type == "lif":
            return {
                'tau_membrane': 20e-3,      # 20ms membrane time constant
                'tau_refractory': 2e-3,     # 2ms refractory period
                'threshold': -50e-3,        # -50mV firing threshold
                'reset_potential': -65e-3,  # -65mV reset potential
                'leak_reversal': -70e-3     # -70mV leak reversal potential
            }
        elif model_type == "adex":
            return {
                'tau_membrane': 9.3e-3,
                'tau_adaptation': 144e-3,
                'leak_reversal': -70.6e-3,
                'threshold': -50.4e-3,
                'spike_slope': 2e-3,
                'adaptation_coupling': 4e-9,
                'spike_adaptation': 80e-12
            }
        # Add other neuron models as needed
 
    async def process_spike_event(self, spike: SpikeEvent):
        """Process incoming spike event through neuromorphic computation"""
 
        # Find target neurons connected to spiking neuron
        target_connections = self.get_postsynaptic_connections(
            source_layer=spike.layer_id,
            source_neuron=spike.neuron_id
        )
 
        # Update membrane potentials of target neurons
        for target_layer, target_neuron, weight in target_connections:
            # Calculate synaptic current based on connection weight
            synaptic_current = weight * spike.spike_amplitude
 
            # Apply synaptic dynamics (exponential decay)
            current_time = spike.timestamp
            synaptic_efficacy = self.calculate_synaptic_efficacy(
                weight=weight,
                last_spike_time=self.get_last_spike_time(spike.layer_id, spike.neuron_id),
                current_time=current_time
            )
 
            # Update target neuron membrane potential
            await self.update_membrane_potential(
                layer_id=target_layer,
                neuron_id=target_neuron,
                synaptic_current=synaptic_current * synaptic_efficacy,
                timestamp=current_time
            )
 
            # Check if target neuron reaches firing threshold
            if self.check_firing_condition(target_layer, target_neuron):
                # Generate new spike event
                new_spike = SpikeEvent(
                    timestamp=current_time + self.get_axonal_delay(spike.layer_id, target_layer),
                    neuron_id=target_neuron,
                    layer_id=target_layer
                )
 
                # Add to event queue for processing
                await self.event_queue.put(new_spike)
 
                # Reset neuron state
                await self.reset_neuron_state(target_layer, target_neuron)
 
        # Update synaptic weights using plasticity rules
        await self.apply_synaptic_plasticity(spike)
 
    async def update_membrane_potential(self, layer_id: int, neuron_id: int,
                                      synaptic_current: float, timestamp: float):
        """Update neuron membrane potential using specified neuron model"""
 
        layer = self.layers[layer_id]
        neuron_params = layer['neuron_parameters']
 
        if layer['neuron_model'] == NeuronModel.LEAKY_INTEGRATE_FIRE:
            # Leaky Integrate-and-Fire dynamics
            dt = timestamp - self.get_last_update_time(layer_id, neuron_id)
 
            # Leak current
            leak_current = (neuron_params['leak_reversal'] -
                          layer['membrane_potentials'][neuron_id]) / neuron_params['tau_membrane']
 
            # Total current
            total_current = leak_current + synaptic_current
 
            # Update membrane potential
            layer['membrane_potentials'][neuron_id] += total_current * dt
 
        elif layer['neuron_model'] == NeuronModel.ADAPTIVE_EXPONENTIAL:
            # Adaptive Exponential Integrate-and-Fire dynamics
            dt = timestamp - self.get_last_update_time(layer_id, neuron_id)
 
            V = layer['membrane_potentials'][neuron_id]
            w = layer['adaptation_variables'][neuron_id]
 
            # Exponential term
            exp_term = neuron_params['spike_slope'] * np.exp(
                (V - neuron_params['threshold']) / neuron_params['spike_slope']
            )
 
            # Membrane potential dynamics
            dV_dt = (-(V - neuron_params['leak_reversal']) + exp_term - w + synaptic_current) / neuron_params['tau_membrane']
 
            # Adaptation variable dynamics
            dw_dt = (neuron_params['adaptation_coupling'] * (V - neuron_params['leak_reversal']) - w) / neuron_params['tau_adaptation']
 
            # Update state variables
            layer['membrane_potentials'][neuron_id] += dV_dt * dt
            layer['adaptation_variables'][neuron_id] += dw_dt * dt
 
    async def apply_synaptic_plasticity(self, spike: SpikeEvent):
        """Apply synaptic plasticity rules for learning and adaptation"""
 
        # Spike-Timing Dependent Plasticity (STDP)
        await self.apply_stdp(spike)
 
        # Homeostatic plasticity
        await self.apply_homeostatic_plasticity(spike.layer_id)
 
        # Structural plasticity (connection formation/elimination)
        if self.config.get('structural_plasticity', False):
            await self.apply_structural_plasticity(spike.layer_id)
 
    async def apply_stdp(self, spike: SpikeEvent):
        """Apply Spike-Timing Dependent Plasticity"""
 
        # Get presynaptic connections (inputs to spiking neuron)
        presynaptic_connections = self.get_presynaptic_connections(
            target_layer=spike.layer_id,
            target_neuron=spike.neuron_id
        )
 
        # Get postsynaptic connections (outputs from spiking neuron)
        postsynaptic_connections = self.get_postsynaptic_connections(
            source_layer=spike.layer_id,
            source_neuron=spike.neuron_id
        )
 
        stdp_window = 20e-3  # 20ms STDP window
 
        # Update weights for presynaptic connections (LTD)
        for pre_layer, pre_neuron, connection_id in presynaptic_connections:
            last_pre_spike = self.get_last_spike_time(pre_layer, pre_neuron)
            if last_pre_spike and (spike.timestamp - last_pre_spike) < stdp_window:
                dt = spike.timestamp - last_pre_spike
                if dt > 0:  # Pre-before-post (LTP)
                    weight_change = self.config['stdp']['A_plus'] * np.exp(-dt / self.config['stdp']['tau_plus'])
                else:  # Post-before-pre (LTD)
                    weight_change = -self.config['stdp']['A_minus'] * np.exp(dt / self.config['stdp']['tau_minus'])
 
                self.update_synaptic_weight(connection_id, weight_change)
 
        # Update weights for postsynaptic connections (LTP)
        for post_layer, post_neuron, connection_id in postsynaptic_connections:
            recent_post_spikes = self.get_recent_spikes(
                layer_id=post_layer,
                neuron_id=post_neuron,
                time_window=stdp_window,
                reference_time=spike.timestamp
            )
 
            for post_spike_time in recent_post_spikes:
                dt = post_spike_time - spike.timestamp
                if abs(dt) < stdp_window:
                    if dt > 0:  # Pre-before-post (LTP)
                        weight_change = self.config['stdp']['A_plus'] * np.exp(-dt / self.config['stdp']['tau_plus'])
                    else:  # Post-before-pre (LTD)
                        weight_change = -self.config['stdp']['A_minus'] * np.exp(dt / self.config['stdp']['tau_minus'])
 
                    self.update_synaptic_weight(connection_id, weight_change)
 
class NeuromorphicAccelerator:
    """Hardware accelerator for neuromorphic computation"""
 
    def __init__(self, chip_config: Dict):
        self.config = chip_config
        self.cores = self.initialize_neuromorphic_cores()
        self.routing_fabric = self.initialize_routing_fabric()
        self.memory_subsystem = self.initialize_memory_subsystem()
        self.power_manager = PowerManager()
 
    def initialize_neuromorphic_cores(self) -> List[Dict]:
        """Initialize neuromorphic processing cores"""
        cores = []
 
        for core_id in range(self.config['num_cores']):
            core = {
                'core_id': core_id,
                'neurons_per_core': self.config['neurons_per_core'],
                'synapses_per_core': self.config['synapses_per_core'],
                'local_memory': self.allocate_local_memory(core_id),
                'spike_buffer': SpikeBuffer(capacity=1024),
                'routing_table': RoutingTable(),
                'learning_engine': LocalLearningEngine(),
                'power_state': 'active'
            }
            cores.append(core)
 
        return cores
 
    async def process_neuromorphic_workload(self, network_definition: Dict,
                                          input_spike_trains: List[SpikeEvent]) -> List[SpikeEvent]:
        """Process neuromorphic workload across multiple cores"""
 
        # Map network to hardware cores
        core_mapping = await self.map_network_to_cores(network_definition)
 
        # Configure routing fabric
        await self.configure_routing_fabric(core_mapping)
 
        # Initialize network state on cores
        for core_id, network_partition in core_mapping.items():
            await self.load_network_partition(core_id, network_partition)
 
        # Process input spike trains
        output_spikes = []
 
        # Create event-driven simulation
        event_scheduler = EventScheduler()
 
        # Schedule input spikes
        for spike in input_spike_trains:
            event_scheduler.schedule_event(spike.timestamp, spike)
 
        # Process events
        while not event_scheduler.empty():
            current_time, event = event_scheduler.get_next_event()
 
            if isinstance(event, SpikeEvent):
                # Route spike to appropriate core
                target_core = self.route_spike_to_core(event)
 
                # Process spike on target core
                core_output = await self.process_spike_on_core(target_core, event)
 
                # Schedule any output spikes
                for output_spike in core_output.output_spikes:
                    event_scheduler.schedule_event(output_spike.timestamp, output_spike)
 
                    # Collect output spikes if they're from output layers
                    if self.is_output_spike(output_spike):
                        output_spikes.append(output_spike)
 
        return output_spikes
 
    async def optimize_power_consumption(self):
        """Implement power optimization strategies"""
 
        # Dynamic voltage and frequency scaling
        for core in self.cores:
            activity_level = await self.measure_core_activity(core['core_id'])
 
            if activity_level < 0.1:  # Low activity
                await self.power_manager.enter_sleep_mode(core['core_id'])
            elif activity_level < 0.5:  # Moderate activity
                await self.power_manager.reduce_voltage_frequency(core['core_id'], factor=0.7)
            else:  # High activity
                await self.power_manager.set_maximum_performance(core['core_id'])
 
        # Clock gating for inactive components
        await self.power_manager.apply_clock_gating()
 
        # Adaptive precision scaling
        await self.optimize_numerical_precision()

class MemristiveDevice:
    """Memristive device model for neuromorphic synapses"""
 
    def __init__(self, device_type: str = "TiO2"):
        self.device_type = device_type
        self.resistance_state = self.initialize_resistance_state()
        self.resistance_bounds = self.get_device_bounds()
        self.switching_dynamics = self.get_switching_characteristics()
 
    def get_device_bounds(self) -> Tuple[float, float]:
        """Get resistance bounds for specific device type"""
        bounds = {
            'TiO2': (1e3, 1e6),      # 1kΩ to 1MΩ
            'HfO2': (5e2, 5e5),      # 500Ω to 500kΩ
            'PCMO': (1e3, 1e7),      # 1kΩ to 10MΩ
            'ReRAM': (1e2, 1e6)      # 100Ω to 1MΩ
        }
        return bounds.get(self.device_type, (1e3, 1e6))
 
    def update_conductance(self, voltage_pulse: float, pulse_duration: float) -> float:
        """Update device conductance based on applied voltage pulse"""
 
        # Nonlinear switching dynamics
        if abs(voltage_pulse) > self.switching_dynamics['threshold_voltage']:
            # Calculate conductance change
            delta_G = self.calculate_conductance_change(voltage_pulse, pulse_duration)
 
            # Apply conductance bounds
            new_conductance = np.clip(
                1/self.resistance_state + delta_G,
                1/self.resistance_bounds[1],  # Min conductance (max resistance)
                1/self.resistance_bounds[0]   # Max conductance (min resistance)
            )
 
            self.resistance_state = 1/new_conductance
 
        return 1/self.resistance_state
 
    def calculate_conductance_change(self, voltage: float, duration: float) -> float:
        """Calculate conductance change using physics-based model"""
 
        # Simplified model based on oxygen vacancy migration
        if voltage > 0:  # SET operation (increase conductance)
            rate = self.switching_dynamics['set_rate'] * np.exp(
                voltage / self.switching_dynamics['voltage_scale']
            )
        else:  # RESET operation (decrease conductance)
            rate = -self.switching_dynamics['reset_rate'] * np.exp(
                abs(voltage) / self.switching_dynamics['voltage_scale']
            )
 
        # Integrate over pulse duration
        delta_G = rate * duration
 
        # Add stochastic variations
        noise_factor = np.random.normal(1.0, self.switching_dynamics['noise_std'])
 
        return delta_G * noise_factor
 
class NeuromorphicMemoryArray:
    """Crossbar array of memristive devices for synaptic weight storage"""
 
    def __init__(self, rows: int, cols: int, device_type: str = "ReRAM"):
        self.rows = rows
        self.cols = cols
        self.devices = [[MemristiveDevice(device_type) for _ in range(cols)]
                       for _ in range(rows)]
        self.peripheral_circuits = self.initialize_peripherals()
 
    async def vector_matrix_multiply(self, input_vector: np.ndarray) -> np.ndarray:
        """Perform analog vector-matrix multiplication"""
 
        # Convert input vector to voltage levels
        input_voltages = self.convert_to_voltages(input_vector)
 
        # Apply voltages to rows
        output_currents = np.zeros(self.cols)
 
        for col in range(self.cols):
            column_current = 0.0
 
            for row in range(self.rows):
                # Current through memristive device
                device_conductance = 1 / self.devices[row][col].resistance_state
                device_current = input_voltages[row] * device_conductance
                column_current += device_current
 
            # Add peripheral circuit effects
            column_current = self.peripheral_circuits.apply_analog_effects(
                col, column_current
            )
 
            output_currents[col] = column_current
 
        # Convert currents back to digital values
        output_values = self.convert_currents_to_digital(output_currents)
 
        return output_values
 
    async def update_weights(self, weight_updates: np.ndarray):
        """Update synaptic weights using memristive device programming"""
 
        for row in range(self.rows):
            for col in range(self.cols):
                weight_change = weight_updates[row, col]
 
                if abs(weight_change) > self.update_threshold:
                    # Calculate required voltage pulse
                    voltage_pulse, duration = self.calculate_programming_pulse(
                        current_weight=1/self.devices[row][col].resistance_state,
                        target_change=weight_change
                    )
 
                    # Apply programming pulse
                    new_conductance = self.devices[row][col].update_conductance(
                        voltage_pulse, duration
                    )
 
                    # Verify programming success
                    if not self.verify_programming_accuracy(row, col, weight_change):
                        # Retry with adjusted parameters
                        await self.retry_programming(row, col, weight_change)