hardware-aware-mixed-signal-snn-framework

name	hardware-aware-mixed-signal-snn-framework
description	Open-source hardware-aware simulation framework for mixed-signal SNNs enabling comparative analysis across neuron models (LIF, HH, AH), synapse types (floating-gate, ReRAM), and architectures. Reports accuracy with hardware metrics (area, power, quantization sensitivity).
tags	["spiking-neural-network","hardware-simulation","mixed-signal","neuromorphic","design-space-exploration","open-source"]
activation_words	["hardware-aware SNN","mixed-signal simulation","neuron model comparison","LIF HH AH","ReRAM synapse","floating-gate","design space exploration","neuromorphic benchmark"]

Hardware-Aware Open-Source Framework for Mixed-Signal SNN Design Space Exploration

Overview

Open-source hardware-aware simulation framework for mixed-signal spiking neural networks that enables comparative analysis across neuron models, synapse types, and architectures while reporting both accuracy and hardware-oriented metrics.

Core Problem

Fragmented SNN Simulation:

• Existing tools capture either biological detail OR hardware efficiency, not both
• No unified framework for cross-layer design space exploration
• Difficult to compare neuron-synapse configurations for specific applications
• Missing hardware metrics (area, power, quantization sensitivity) in standard SNN tools

Framework Architecture

Supported Neuron Models

1. Leaky Integrate-and-Fire (LIF)

   • Simplest model, fastest simulation
   • Good for large-scale networks
   • Limited biological realism

2. Hodgkin-Huxley (HH)

   • Biologically detailed ion channel dynamics
   • Captures realistic spike shapes
   • Computationally expensive

3. Axon-Hillock (AH)

   • Intermediate complexity
   • Balances realism and efficiency
   • Captures spike initiation zone dynamics

Supported Synapse Types

1. Floating-Gate Transistors

   • Non-volatile analog storage
   • Tunable weight updates
   • Mature CMOS technology

2. ReRAM Devices

   • Resistive switching memory
   • High density, low power
   • Emerging technology with non-idealities

Hardware Metrics

Reported for Each Configuration:

• Silicon Area: Estimated from transistor count and layout
• Power Consumption: Dynamic + static power estimation
• Quantization Sensitivity: Accuracy vs. precision tradeoffs
• Hardware Fidelity: How well simulation captures non-ideal behavior

Implementation

PyTorch Integration

import torch
import spikingjelly  # or custom framework

class HardwareAwareSNN:
    def __init__(self, neuron_model='LIF', synapse_type='floating_gate'):
        self.neuron_model = self._build_neuron(neuron_model)
        self.synapse = self._build_synapse(synapse_type)
        self.hardware_metrics = HardwareMetrics()
    
    def _build_neuron(self, model_type):
        if model_type == 'LIF':
            return LIFNeuron(tau_mem=20.0, v_threshold=1.0)
        elif model_type == 'HH':
            return HHNeuron(g_Na=120.0, g_K=36.0, g_L=0.3)
        elif model_type == 'AH':
            return AHNeuron(compartment_params={...})
    
    def _build_synapse(self, synapse_type):
        if synapse_type == 'floating_gate':
            return FloatingGateSynapse(device_params={...})
        elif synapse_type == 'ReRAM':
            return ReRAMSynapse(hrs_resistance=1e6, lrs_resistance=1e3)
    
    def forward():
        
        synaptic_current = .synapse(spikes)
        membrane_potential = .neuron_model(synaptic_current)
        output_spikes = .neuron_model.fire(membrane_potential)
         output_spikes
    
     ():
        area = .hardware_metrics.estimate_area()
        power = .hardware_metrics.estimate_power()
        quant_sensitivity = .hardware_metrics.quantization_analysis()
         {: area, : power, : quant_sensitivity}

Design Space Exploration

# Example: Sweep across configurations
configs = []
for neuron in ['LIF', 'HH', 'AH']:
    for synapse in ['floating_gate', 'ReRAM']:
        for precision in [8, 16, 32]:
            configs.append({
                'neuron': neuron,
                'synapse': synapse,
                'precision': precision
            })

results = []
for config in configs:
    model = HardwareAwareSNN(config['neuron'], config['synapse'])
    model.set_precision(config['precision'])
    
    # Train and evaluate
    accuracy = train_and_evaluate(model, dataset='N-MNIST')
    
    # Get hardware metrics
    hw_metrics = model.compute_hardware_metrics()
    
    results.append({
        'config': config,
        'accuracy': accuracy,
        **hw_metrics
    })

# Analyze tradeoffs
analyze_pareto_frontier(results)

Benchmarks

Standard Neuromorphic Datasets

1. N-MNIST

   • Spiking version of MNIST
   • 3 event channels (ON, OFF, background)
   • 60k training, 10k test samples

2. DVS Gesture

   • Dynamic Vision Sensor recordings
   • 11 hand gesture classes
   • Real-world event camera data

3. Spiking Heidelberg Digits (SHD)

   • Spoken digits (0-9) in English/German
   • 20ms binned audio spectrograms
   • Temporal classification task

Reported Metrics

For each model-dataset configuration:

• Classification Accuracy: Top-1 accuracy on test set
• Silicon Area: mm² estimated from transistor count
• Power Consumption: mW during inference
• Quantization Sensitivity: Accuracy drop at lower precision

Design Space Exploration

Configuration Parameters

1. Neuron Model Selection

   • LIF: Fast, scalable, less biological
   • HH: Detailed, slow, biologically realistic
   • AH: Balanced complexity

2. Synapse Device Choice

   • Floating-gate: Mature, tunable, moderate density
   • ReRAM: High density, low power, emerging tech

3. Precision Tradeoffs

   • 8-bit: Lowest power/area, potential accuracy loss
   • 16-bit: Balanced
   • 32-bit: Highest accuracy, more resources

4. Architecture Decisions

   • Number of layers
   • Neurons per layer
   • Connectivity pattern (feedforward, recurrent)

Pareto Analysis

def analyze_pareto_frontier(results):
    """
    Identify configurations on Pareto frontier of accuracy vs. energy
    """
    pareto_configs = []
    for r in results:
        dominated = False
        for other in results:
            if (other['accuracy'] >= r['accuracy'] and 
                other['power'] <= r['power'] and
                (other['accuracy'] > r['accuracy'] or other['power'] < r['power'])):
                dominated = True
                break
        if not dominated:
            pareto_configs.append(r)
    return pareto_configs

Key Findings

Neuron Model Impact

• LIF: Best for large-scale, energy-constrained applications
• HH: Necessary when biological realism is critical
• AH: Good middle ground for many applications

Synapse Device Comparison

• Floating-gate: More predictable, easier to train
• ReRAM: Higher density but more non-idealities to manage

Precision Tradeoffs

• 8-bit: Often sufficient for N-MNIST, significant area/power savings
• 16-bit: Recommended default for most applications
• 32-bit: Only necessary for very sensitive tasks

Applications

Edge AI Deployment

• Selecting optimal configuration for battery-powered devices
• Balancing accuracy and energy for always-on inference

Neuromorphic Chip Design

• Informing architecture decisions before fabrication
• Validating design choices against benchmarks

Algorithm-Hardware Co-Design

• Co-optimizing SNN algorithms with hardware constraints
• Identifying hardware-aware training objectives

Related Work

• snntorch: Software SNN simulation (no hardware metrics)
• Brian2: Biological neural simulation (not hardware-focused)
• NEST: Large-scale simulation (not mixed-signal)
• Intel Lava: Neuromorphic compilation (fixed architecture)

Limitations

• Simulation Accuracy: Hardware metrics are estimates, not measurements
• Device Variability: Real devices have more variation than modeled
• Scalability: Full-chip simulation still computationally expensive
• Technology Nodes: Metrics depend on specific process technology

Future Work

• Integration with actual silicon measurements
• Support for more neuron/synapse models
• Automated configuration optimization
• Multi-objective optimization beyond accuracy-power

Activation

hardware-aware SNN, mixed-signal simulation, neuron model comparison, LIF HH AH, ReRAM synapse, floating-gate, design space exploration, neuromorphic benchmark, PyTorch SNN, hardware metrics

arXiv Reference

• ID: 2607.06456
• Title: A Hardware-Aware Open-Source Framework for Design Space Exploration of Mixed-Signal Spiking Neural Networks
• Authors: Sayma Nowshin Chowdhury, Vineeta Nair, Taseen Forhad
• Categories: eess.SP, cs.NE
• Published: 2026-07-07