This paper proposes an ultra-low power, mixed-bit-width sparse convolutional neural network (CNN) accelerator to accelerate ventricular arrhythmia (VA) detection. The chip achieves 50% sparsity in a quantized 1D CNN using a sparse processing element (SPE) architecture. Measurement on the prototype chip TSMC 40nm CMOS low-power (LP) process for the VA classification task demonstrates that it consumes 10.60 $\mu$W of power while achieving a performance of 150 GOPS and a diagnostic accuracy of 99.95%. The computation power density is only 0.57 $\mu$W/mm$^2$, which is 14.23X smaller than state-of-the-art works, making it highly suitable for implantable and wearable medical devices.

翻译：本文提出了一种超低功耗、混合位宽的稀疏卷积神经网络（CNN）加速器，用于加速室性心律失常（VA）检测。该芯片采用稀疏处理单元（SPE）架构，在量化的一维CNN中实现了50%的稀疏度。在VA分类任务上，基于台积电40纳米CMOS低功耗（LP）工艺的原型芯片实测表明，其功耗仅为10.60 μW，同时实现了150 GOPS的性能和99.95%的诊断准确率。其计算功率密度仅为0.57 μW/mm²，比现有先进工作低14.23倍，因此非常适用于植入式和可穿戴医疗设备。