FeNN-DMA: A RISC-V SoC for SNN acceleration

• URL: http://arxiv.org/abs/2511.00732v1
• Date: Sat, 01 Nov 2025 22:59:54 GMT
• Title: FeNN-DMA: A RISC-V SoC for SNN acceleration
• Authors: Zainab Aizaz, James C. Knight, Thomas Nowotny,
• Abstract summary: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs)<n>We show that FeNN-DMA has comparable resource usage and energy requirements to state-of-the-art fixed-function SNN accelerators.<n>We demonstrate state-of-the-art classification accuracy on simulating the Spiking Heidelberg Digits and Neuromorphic MNIST tasks.
• Score: 2.560446860313122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks such as keyword spotting and video classification. However, SNNs have a much lower arithmetic intensity than ANNs and are therefore not well-matched to standard accelerators like GPUs and TPUs. Field Programmable Gate Arrays(FPGAs) are designed for such memory-bound workloads and here we develop a novel, fully-programmable RISC-V-based system-on-chip (FeNN-DMA), tailored to simulating SNNs on modern UltraScale+ FPGAs. We show that FeNN-DMA has comparable resource usage and energy requirements to state-of-the-art fixed-function SNN accelerators, yet it is capable of simulating much larger and more complex models. Using this functionality, we demonstrate state-of-the-art classification accuracy on the Spiking Heidelberg Digits and Neuromorphic MNIST tasks.

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