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Computer Science > Neural and Evolutionary Computing

arXiv:2507.07284 (cs)
[Submitted on 9 Jul 2025 (v1), last revised 23 Jul 2025 (this
version, v2)]

Title:A Robust, Open-Source Framework for Spiking Neural Networks on
Low-End FPGAs

Authors:Andrew Fan, Simon D. Levy
View a PDF of the paper titled A Robust, Open-Source Framework for
Spiking Neural Networks on Low-End FPGAs, by Andrew Fan and Simon D.
Levy
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    Abstract:As the demand for compute power in traditional neural
    networks has increased significantly, spiking neural networks
    (SNNs) have emerged as a potential solution to increasingly
    power-hungry neural networks. By operating on 0/1 spikes emitted
    by neurons instead of arithmetic multiply-and-accumulate
    operations, SNNs propagate information temporally and spatially,
    allowing for more efficient compute power. To this end, many
    architectures for accelerating and simulating SNNs have been
    developed, including Loihi, TrueNorth, and SpiNNaker. However,
    these chips are largely inaccessible to the wider community.
    Field programmable gate arrays (FPGAs) have been explored to
    serve as a middle ground between neuromorphic and
    non-neuromorphic hardware, but many proposed architectures
    require expensive high-end FPGAs or target a single SNN topology.
    This paper presents a framework consisting of a robust SNN
    acceleration architecture and a Pytorch-based SNN model compiler.
    Targeting any-to-any and/or fully connected SNNs, the FPGA
    architecture features a synaptic array that tiles across the SNN
    to propagate spikes. The architecture targets low-end FPGAs and
    requires very little (6358 LUT, 40.5 BRAM) resources. The
    framework, tested on a low-end Xilinx Artix-7 FPGA at 100 MHz,
    achieves competitive speed in recognizing MNIST digits (0.52 ms/
    img). Further experiments also show accurate simulation of hand
    coded any-to-any spiking neural networks on toy problems. All
    code and setup instructions are available at this https URL}{\
    texttt{this https URL.

Subjects: Neural and Evolutionary Computing (cs.NE)
Cite as:  arXiv:2507.07284 [cs.NE]
          (or arXiv:2507.07284v2 [cs.NE] for this version)
          https://doi.org/10.48550/arXiv.2507.07284
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Simon Levy [view email]
[v1] Wed, 9 Jul 2025 21:08:28 UTC (1,193 KB)
[v2] Wed, 23 Jul 2025 00:13:53 UTC (763 KB)
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