TCN-CUTIE: A 1036 TOp/s/W, 2.72 uJ/Inference, 12.2 mW All-Digital Ternary Accelerator in 22 nm FDX Technology (2212.00688v1)

Abstract: Tiny Machine Learning (TinyML) applications impose uJ/Inference constraints, with a maximum power consumption of tens of mW. It is extremely challenging to meet these requirements at a reasonable accuracy level. This work addresses the challenge with a flexible, fully digital Ternary Neural Network (TNN) accelerator in a RISC-V-based System-on-Chip (SoC). Besides supporting Ternary Convolutional Neural Networks, we introduce extensions to the accelerator design that enable the processing of time-dilated Temporal Convolutional Neural Networks (TCNs). The design achieves 5.5 uJ/Inference, 12.2 mW, 8000 Inferences/sec at 0.5 V for a Dynamic Vision Sensor (DVS) based TCN, and an accuracy of 94.5 % and 2.72 uJ/Inference, 12.2 mW, 3200 Inferences/sec at 0.5 V for a non-trivial 9-layer, 96 channels-per-layer convolutional network with CIFAR-10 accuracy of 86 %. The peak energy efficiency is 1036 TOp/s/W, outperforming the state-of-the-art silicon-proven TinyML quantized accelerators by 1.67x while achieving competitive accuracy.