基于RISC-V的向量处理器FeNN，专为FPGA上的脉冲神经网络加速设计。
FeNN: A RISC-V vector processor for Spiking Neural Network acceleration
- 采用RISC-V软向量架构，适配FPGA实现可编程的脉冲神经网络计算。
- 通过随机舍入与饱和处理，在低硬件开销下保持高数值精度。
- 单核性能超越嵌入式GPU和Loihi系统，适用于边缘到云端部署。
脉冲神经网络（SNNs）有潜力显著降低人工智能系统的能耗。然而，主流加速器如GPU和TPU专为标准人工神经网络（ANNs）的高算术强度设计，不适用于SNN仿真。FPGA因具备高片外内存带宽和大量片上内存，更适合低算术强度的应用。本文提出一种新型基于RISC-V的软向量处理器FeNN，专为在FPGA上模拟SNN而设计。与多数专用类脑硬件不同，FeNN完全可编程，可与运行在从边缘到云端的通用计算机应用集成。我们证明，通过使用随机舍入和饱和机制，FeNN可在低硬件利用率下实现高数值精度；单个FeNN核心的SNN分类器仿真速度优于嵌入式GPU和Loihi类脑系统。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) have the potential to drastically reduce the energy requirements of AI systems. However, mainstream accelerators like GPUs and TPUs are designed for the high arithmetic intensity of standard ANNs so are not well-suited to SNN simulation. FPGAs are well-suited to applications with low arithmetic intensity as they have high off-chip memory bandwidth and large amounts of on-chip memory. Here, we present a novel RISC-V-based soft vector processor (FeNN), tailored to simulating SNNs on FPGAs. Unlike most dedicated neuromorphic hardware, FeNN is fully programmable and designed to be integrated with applications running on standard computers from the edge to the cloud. We demonstrate that, by using stochastic rounding and saturation, FeNN can achieve high numerical precision with low hardware utilisation and that a single FeNN core can simulate an SNN classifier faster than both an embedded GPU and the Loihi neuromorphic system.
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