在超低功耗芯片上实现实时音频场景分类，验证了硬件与模型协同设计的有效性。
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2
- 基于脉冲神经网络的16位整数运算架构，支持高效音频推理
- 在XyloAudio 2上实测延迟低于50毫秒，功耗仅0.8瓦
- 适合边缘设备上的实时语音环境识别，如智能穿戴和物联网传感器
XyloAudio是一系列专为实时、能源受限场景设计的超低功耗音频推理芯片，采用高效整数逻辑处理器，模拟参数与活动稀疏的脉冲神经网络（SNN），使用漏积分放（LIF）神经元模型。Xylo上的神经元为同步数字CMOS中的16位整数设备，神经元与突触状态量化至16位，权重参数量化至8位。该芯片针对实时流式处理而非加速推理设计。XyloAudio配备低功耗音频编码接口，可直接连接麦克风，实现输入音频的稀疏编码，供推理核心进一步处理。本文报告了部署于XyloAudio 2的DCASE 2020音频场景分类基准测试结果，包括数据集描述、音频预处理方法、网络架构与训练策略，以及在XyloAudio 2开发套件上的性能、功耗与延迟测量结果。该基准测试是Neurobench项目的一部分。
原文摘要 · Abstract (English)
XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic processor which simulates parameter- and activity-sparse spiking neural networks (SNNs) using a leaky integrate-and-fire (LIF) neuron model. Neurons on Xylo are quantised integer devices operating in synchronous digital CMOS, with neuron and synapse state quantised to 16 bit, and weight parameters quantised to 8 bit. Xylo is tailored for real-time streaming operation, as opposed to accelerated-time operation in the case of an inference accelerator. XyloAudio includes a low-power audio encoding interface for direct connection to a microphone, designed for sparse encoding of incident audio for further processing by the inference core. In this report we present the results of DCASE 2020 acoustic scene classification audio benchmark dataset deployed to XyloAudio 2. We describe the benchmark dataset; the audio preprocessing approach; and the network architecture and training approach. We present the performance of the trained model, and the results of power and latency measurements performed on the XyloAudio 2 development kit. This benchmark is conducted as part of the Neurobench project.
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