A Scalable Pipelined Time-Domain DTW Engine for Time-Series Classification Using Multibit Time Flip-Flops with 140Giga-Cell-Updates/s Throughput

📋 论文概要

提出了一种可扩展的流水线时域动态时间规整（DTW）引擎，采用多比特时间触发器（MTFF）实现高速时间序列分类。解决了传统DTW加速器中单比特操作和固定序列长度的限制，实现了140Giga-C（每秒1400亿次比较）的处理能力。

💡 主要创新点

• 首创流水线时域DTW架构，利用时间域计算代替传统数字逻辑，降低功耗并提高吞吐量。
• 提出多比特时间触发器（MTFF），支持多比特精度的时间差计算，突破单比特操作局限。
• 实现了可扩展的序列长度处理，支持可变长度的时间序列分类。

🏷 关键词

📄 原文摘要

Dynamic time warping (DTW), a variant of the dynamic programming algorithm, is widely used for time series classification [1]. Its strong capability for distance measurement for variable-speed temporal sequences makes DTW a popular method for time-series classification in broad applications, such as ECG diagnosis, motion detection, DNA sequencing, etc. [1]. Several efforts have proposed for accelerating the operation of DTW, including a recent demonstration of time-based design in DNA sequencing [2]. However, the demonstration was confined to single-bit operations, a fixed sequence length and low throughput due to nonpipelined operation and a large single-bit delay. To overcome such challenges, this work presents a general-purpose DTW engine for time-series classification using time-domain computing. Pipelined operation is enabled by a time flip-flop (TFF) leading to order-of-magnitude improvements in throughput and a scalable

👥 作者与机构

Zhengyu Chen, Jie Gu

Northwestern University, Evanston, IL