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 present 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 supports more complex neuron models and network topologies, and can simulate up to 16 thousand neurons and 256 million synapses per core. Using this functionality, we demonstrate state-of-the-art classification accuracy on the Spiking Heidelberg Digits, Neuromorphic MNIST and Braille tactile classification tasks.

翻译：脉冲神经网络（SNNs）是标准人工神经网络（ANNs）的一种有前景且高能效的替代方案，尤其适用于关键词检测和视频分类等时空任务。然而，SNNs的算术强度远低于ANNs，因此与GPU和TPU等标准加速器并不匹配。现场可编程门阵列（FPGAs）专为这类内存受限的工作负载而设计，本文提出了一种新颖的、基于RISC-V的完全可编程片上系统（FeNN-DMA），专为在现代UltraScale+ FPGAs上模拟SNNs而定制。我们证明，FeNN-DMA的资源占用和能耗需求与最先进的固定功能SNN加速器相当，同时支持更复杂的神经元模型和网络拓扑结构，每个核心可模拟多达1.6万个神经元和2.56亿个突触。利用此功能，我们在Spiking Heidelberg Digits、神经形态MNIST和盲文触觉分类任务上展示了最先进的分类准确率。