Biological Intuition on Digital Hardware: An RTL Implementation of Poisson-Encoded SNNs for Static Image Classification

TL;DR

This paper introduces a hardware-efficient, Poisson-encoded SNN core in SystemVerilog for static image classification, emphasizing low power and fast convergence suitable for edge AI devices.

Contribution

It presents a novel RTL implementation of a Poisson-encoded SNN with active pruning and fixed-point neuron models, advancing neuromorphic hardware design.

Findings

Achieves 89% accuracy on digit classification

Reduces power consumption through neuron pruning

Converges rapidly within limited timesteps

Abstract

The deployment of Artificial Intelligence on edge devices (TinyML) is often constrained by the high power consumption and latency associated with traditional Artificial Neural Networks (ANNs) and their reliance on intensive Matrix-Multiply (MAC) operations. Neuromorphic computing offers a compelling alternative by mimicking biological efficiency through event-driven processing. This paper presents the design and implementation of a cycle-accurate, hardware-oriented Spiking Neural Network (SNN) core implemented in SystemVerilog. Unlike conventional accelerators, this design utilizes a Leaky Integrate-and-Fire (LIF) neuron model powered by fixed-point arithmetic and bit-wise primitives (shifts and additions) to eliminate the need for complex floating-point hardware. The architecture features an on-chip Poisson encoder for stochastic spike generation and a novel active pruning mechanism that dynamically disables neurons post-classification to minimize dynamic power consumption. We demonstrate the hardware's efficacy through a fully connected layer implementation targeting digit classification. Simulation results indicate that the design achieves rapid convergence (89% accuracy) within limited timesteps while maintaining a significantly reduced computational footprint compared to traditional dense architectures. This work serves as a foundational building block for scalable, energy-efficient neuromorphic hardware on FPGA and ASIC platforms.