高吞吐脉冲神经网络芯片支持突触延迟模拟，适合边缘低功耗语音识别。
A High-Throughput Spiking Neural Network Processor Enabling Synaptic Delay Emulation
- 采用多核流水线架构，实现突触延迟的并行计算。
- 在SHD数据集上达93.4%准确率，每秒处理104个样本。
- 适用于边缘设备的低功耗实时语音关键词检测。
突触延迟在神经网络动力学中对复杂时空信息的整合与处理具有重要意义。本文提出一种高吞吐脉冲神经网络（SNN）处理器，支持面向边缘应用的突触延迟模拟。该处理器采用多核流水线架构，配备并行计算单元，可实时处理突触延迟相关的计算负载。我们在PYNQ Z2 FPGA平台上实现了该处理器的SoC原型，并基于Spiking Heidelberg Digits（SHD）基准测试其在低功耗关键词识别任务中的性能。结果表明，该处理器在典型工作频率125 MHz下实现93.4%的部署准确率，平均吞吐量为104样本/秒，功耗为282 mW。
原文摘要 · Abstract (English)
Synaptic delay has attracted significant attention in neural network dynamics for integrating and processing complex spatiotemporal information. This paper introduces a high-throughput Spiking Neural Network (SNN) processor that supports synaptic delay-based emulation for edge applications. The processor leverages a multicore pipelined architecture with parallel compute engines, capable of real-time processing of the computational load associated with synaptic delays. We develop a SoC prototype of the proposed processor on PYNQ Z2 FPGA platform and evaluate its performance using the Spiking Heidelberg Digits (SHD) benchmark for low-power keyword spotting tasks. The processor achieves 93.4% accuracy in deployment and an average throughput of 104 samples/sec at a typical operating frequency of 125 MHz and 282 mW power consumption.
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