High-Throughput Dynamic Time Warping Accelerator for Time-Series Classification With Pipelined Mixed-Signal Time-Domain Computing
Zhengyu Chen, Jie Gu
Abstract
Time-series classification (TSC) is a challenging problem in machine learning and significant efforts have been made to improve its speed and computation efficiency. Among various approaches, dynamic time warping (DTW) algorithm is one of the most prevalent methods for TSC due to its succinctness and generality. To improve the throughput of the operation, this work presents a mixed-signal DTW accelerator utilizing mixed-signal time-domain (TD) computing where signals are encoded and processed using time pulses. A pipelined operation is enabled by a specially designed time flip-flop (TFF) circuit leading to dramatic improvements in performance and scalability of the operation. A 65-nm CMOS test chip was implemented and measured. The results show more than 9× improvements in throughput compared with prior work on TSC. As most existing TD designs suffer from the lack of TD storage elements, this work utilizes sequential circuit elements in TD computing extending the capability of time-based circuits.