circuit-level-spiking-neuron-robustness

name	circuit-level-spiking-neuron-robustness
description	Circuit-level spiking neuron model for hardware robustness analysis. Studies how transistor-level variations affect SNN reliability on neuromorphic chips. Activation: circuit-level SNN, neuromorphic hardware reliability, transistor variation spiking, hardware spiking neuron, CMOS spiking, SNN fault tolerance
version	1.0.0
metadata	{"hermes":{"tags":["spiking-neural-networks","neuromorphic-hardware","robustness","circuit-design"],"source_paper":"arXiv:2502.09876"}}

Circuit-Level Spiking Neuron Models for Robustness Analysis

Overview

Analyzes how circuit-level hardware variations (transistor threshold voltage shifts, process variations, temperature drift) affect computational reliability of spiking neuron implementations on neuromorphic chips. Bridges device-level non-idealities to network-level accuracy degradation.

Core Concepts

Hardware-Aware LIF Neuron

import torch, torch.nn as nn

class HardwareAwareLIF(nn.Module):
    def __init__(self, tau_mem=10.0, v_threshold=1.0, tau_syn=5.0, variation_std=0.05):
        super().__init__()
        self.tau_mem = tau_mem * (1 + torch.randn(1) * variation_std)
        self.v_threshold = v_threshold * (1 + torch.randn(1) * variation_std)
        self.tau_syn = tau_syn * (1 + torch.randn(1) * variation_std)

    def forward(self, x, time_steps=10):
        v = torch.zeros_like(x)
        i_syn = torch.zeros_like(x)
        spikes = []
        for t in range(time_steps):
            i_syn = i_syn * torch.exp(-1/self.tau_syn) + x
            v = v * torch.exp(-1/self.tau_mem) + i_syn
            spike = (v >= self.v_threshold).float()
            v = v * (1 - spike)
            spikes.append(spike)
        return torch.stack(spikes).sum(0)

Variation-Aware Simulation

import numpy as np

def simulate_variation(neuron_params, n_trials=100):
    results = []
    for _ in range(n_trials):
        varied = {k: v * (1 + np.random.normal(0, 0.05)) for k, v in neuron_params.items()}
        accuracy = evaluate_network(varied)
        results.append(accuracy)
    return np.mean(results), np.std(results)

Key Findings

• Membrane time constant is most variation-sensitive parameter
• Threshold voltage mismatch causes 3-8% accuracy drop at 5% variation
• Synaptic weight quantization has less impact than neuron parameter drift
• Redundant neuron ensembles reduce sensitivity by 40-60%

Pitfalls

1. Variation models must match foundry PDK data
2. Temperature effects compound process variations
3. Monte Carlo needs 100+ trials for significance
4. Analog vs digital implementations have different failure modes

References

• arXiv:2502.09876
• Related: snn-internal-noise-analysis, quantized-snn-hardware-optimization

Activation Keywords

• circuit-level SNN, neuromorphic hardware reliability, transistor variation spiking, hardware spiking neuron, CMOS spiking, SNN fault tolerance