A 40-NM 54-MW 3×-real-time VLSI processor for 60-kWord continuous speech recognition

Abstract

This paper describes a low-power VLSI chip for speaker-independent 60-kWord continuous speech recognition based on a context-dependent Hidden Markov Model (HMM). We implement parallel and pipelined architecture for GMM computation and Viterbi processing. It includes a 8-path Viterbi transition architecture to maximize the processing speed and adopts tri-gram language model to improve the recognition accuracy. A two-level cache architecture is implemented for the demo system. The test chip, fabricated in 40 nm CMOS technology, occupies 1.77 mm × 2.18 mm containing 2.98 M transistors for logic and 4.29 Mbit on-chip memory. The measured results show that our implementation achieves 25% required frequency reduction (62.5 MHz) and 26% power consumption reduction (54.8 mW) for 60 k-Word real-time continuous speech recognition compared to the previous work. This chip can maximally process 3.02× and 2.25× times faster than real-time at 200 MHz using the bigram and trigram language models, respectively.