Abstract

Recent advances in machine learning show the superior behavior of temporal convolutional networks (TCNs) and especially their combination with residual networks (TC-ResNet) for intelligent sensor signal processing in comparison to classical CNNs and LSTMs. In this paper, we propose UltraTrail, a configurable, ultra-low power TC-ResNet AI accelerator for sensor signal processing and its application to efficient keyword spotting. Following a strict hardware/model co-design approach, we have derived an optimized low-power hardware architecture for generalized TC-ResNet topologies consisting of a configurable array of processing elements and a distributed memory with dynamic content re-allocation. We additionally extend the network with conditional computing to reduce the number of operations during inference and to provide the possibility for power-gating. The final accelerator implementation in Globalfoundries’ 22FDX technology achieves a power consumption of 8.2 µW for the task of always-on keyword spotting meeting the real-time requirement of 100 ms per inference with an accuracy of 93 % on the Google Speech Command Dataset.