Energy-Efficient Implementation of Spiking Recurrent Cells on FPGA

TL;DR

This paper presents an FPGA implementation of Spiking Recurrent Cells (SRC) in SNNs, balancing biological plausibility and hardware efficiency, with demonstrated energy savings and competitive accuracy on MNIST.

Contribution

The work introduces a simplified, hardware-friendly SRC neuron model implemented in VHDL, enabling efficient, accurate SNNs on FPGA with reduced energy consumption.

Findings

Achieved 96.31% accuracy on MNIST with FPGA implementation.

Reduced energy per digit to 0.45-0.55 mJ by quantizing weights and shortening spiking traces.

Demonstrated robustness of SRC neurons with direct LUT weight storage.

Abstract

Spiking Neural Networks (SNNs) can reduce energy consumption compared to conventional Artificial Neural Networks (ANNs) when spiking activity is sparse and the neuron model is hardware-friendly. However, biologically faithful models are often too costly to implement on FPGAs, whereas very simple models (e.g., IR/LIF) sacrifice part of the neuronal dynamics. In this work, we present an FPGA accelerator for an SNN using Spiking Recurrent Cell (SRC) neurons, providing a trade-off between biological plausibility and hardware cost. We propose a set of mathematical simplifications that remove costly unary operators (\textit{tanh}, \textit{exp}) and avoid floating-point arithmetic through scaling and piecewise-defined approximations. The complete network is implemented in VHDL and validated using spiking traces derived from the MNIST dataset. The weight matrices computed off-line are stored directly in LUT-registers without any adaptation. This demonstrates the robustness of SRC cells. Experiments were conducted on an Artix-7 XC7A200T clocked at 100 MHz. The reference implementation achieves 96.31\% accuracy with a 220-image spiking trace and a processing time of 1.7424 ms per digit. We then investigate accuracy/energy trade-offs by reducing the spiking trace length and quantizing synaptic weights down to 4 bits, achieving 93.32\% accuracy at 0.55 mJ per digit (55 images, 5-bit weights) and 92.89\% at 0.45 mJ (44 images, 4-bit weights). These results show that SRC-based SNNs can deliver competitive performance with reduced energy consumption, while preserving richer neuronal dynamics than standard LIF/IR models.