A 6mW 5K-Word Real-Time Speech Recognizer Using WFST Models

📋 论文概要

该论文提出了一种基于加权有限状态转换器（WFST）模型的低功耗实时语音识别硬件加速器，在6mW功耗下实现了5K词库的实时解码，解决了可穿戴设备等场景下云端语音识别功耗和带宽过高的问题。

💡 主要创新点

• 采用WFST模型进行硬件加速，实现了高效的语音解码器，显著降低了功耗和内存带宽需求。
• 通过优化的硬件架构和算法，在6mW功耗下达到实时解码性能，相比之前54mW的方案大幅提升能效。

🏷 关键词

📄 原文摘要

Hardware-accelerated speech recognition is needed to supplement today’s cloud-based systems in power- and bandwidth-constrained scenarios such as wearable electronics. With efficient hardware speech decoders, client devices can seamlessly transition between cloud-based and local tasks depending on the availability of power and networking. Most previous efforts in hardware speech decoding [1–2] focused primarily on faster decoding rather than low-power devices operating at real-time speed. More recently, [3] demonstrated real-time decoding using 54mW and 82MB/s memory bandwidth, though their architectural optimizations are not easily generalized to the weighted finite-state transducer (WFST) models used by state-of-the-art software decoders. This paper presents a 6mW speech recognition ASIC that uses WFST search networks and performs end-to-end decoding from audio input to text output. Algorithms and data structures developed for software speech decoders are also

👥 作者与机构

Michael Price, James Glass, Anantha P. Chandrakasan

Massachusetts Institute of Technology, Cambridge, MA