Low-power automatic speech recognition through a mobile GPU and a Viterbi accelerator

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatic speech recognition (ASR) has become a core technology for mobile devices. Delivering real-time and accurate ASR has a huge computational cost, which is challenging to achieve in tightly energy-constrained platforms such as mobile devices. A state-of-the-art ASR pipeline consists of a deep neural network (DNN) that converts the audio signal into phonemes' probabilities, followed by a Viterbi search that uses these probabilities to generate a sequence of words. In this article, the authors propose an ASR system for low-power devices that combines a mobile GPU for the DNN with a dedicated hardware accelerator for the Viterbi search. DNN evaluation is easy to parallelize and, hence, it achieves high energy efficiency on a mobile GPU. On the other hand, the Viterbi search is difficult to parallelize, and it represents the main bottleneck for ASR, so the authors propose a hardware accelerator to dramatically reduce its energy requirements while increasing performance. Their proposal outperforms traditional solutions running on the CPU by orders of magnitude. Compared to a GPU-only system, their hybrid scheme combining the GPU and the accelerator improves performance by 5.25 times, while reducing energy by 2.05 times.Peer Reviewe