NEXUS: Bit-Exact ANN-to-SNN Equivalence via Neuromorphic Gate Circuits with Surrogate-Free Training

TL;DR

NEXUS introduces a novel neuromorphic framework that achieves exact ANN-to-SNN equivalence using logic gates and surrogate-free training, resulting in identical outputs and significant energy savings on neuromorphic hardware.

Contribution

It constructs all arithmetic operations from IF neuron logic gates for bit-exact ANN-to-SNN conversion without approximation or heuristics.

Findings

Achieves identical task accuracy with zero degradation.

Demonstrates 27-168,000× energy reduction on neuromorphic hardware.

Maintains 100% accuracy across various decay factors and tolerates synaptic noise.

Abstract

Spiking Neural Networks (SNNs) promise energy-efficient computing through event-driven sparsity, yet all existing approaches sacrifice accuracy by approximating continuous values with discrete spikes. We propose NEXUS, a framework that achieves bit-exact ANN-to-SNN equivalence -- not approximate, but mathematically identical outputs. Our key insight is constructing all arithmetic operations, both linear and nonlinear, from pure IF neuron logic gates that implement IEEE-754 compliant floating-point arithmetic. Through spatial bit encoding (zero encoding error by construction), hierarchical neuromorphic gate circuits (from basic logic gates to complete transformer layers), and surrogate-free STE training (exact identity mapping rather than heuristic approximation), NEXUS produces outputs identical to standard ANNs up to machine precision. Experiments on models up to LLaMA-2 70B demonstrate identical task accuracy (0.00% degradation) with mean ULP error of only 6.19, while achieving 27-168,000$\times$ energy reduction on neuromorphic hardware. Crucially, spatial bit encoding's single-timestep design renders the framework inherently immune to membrane potential leakage (100% accuracy across all decay factors $\beta\in[0.1,1.0]$), while tolerating synaptic noise up to $\sigma=0.2$ with >98% gate-level accuracy.