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arXiv · 2305.07778

Accelerator-Aware Training for Transducer-Based Speech Recognition

Suhaila M. Shakiah·Rupak Vignesh Swaminathan·Hieu Duy Nguyen·Raviteja Chinta·Tariq Afzal·Nathan Susanj·Athanasios Mouchtaris·Grant P. Strimel·Ariya Rastrow
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Abstract

Machine learning model weights and activations are represented in full-precision during training. This leads to performance degradation in runtime when deployed on neural network accelerator (NNA) chips, which leverage highly parallelized fixed-point arithmetic to improve runtime memory and latency. In this work, we replicate the NNA operators during the training phase, accounting for the degradation due to low-precision inference on the NNA in back-propagation. Our proposed method efficiently emulates NNA operations, thus foregoing the need to transfer quantization error-prone data to the Central Processing Unit (CPU), ultimately reducing the user perceived latency (UPL). We apply our approach to Recurrent Neural Network-Transducer (RNN-T), an attractive architecture for on-device streaming speech recognition tasks. We train and evaluate models on 270K hours of English data and show a 5-7% improvement in engine latency while saving up to 10% relative degradation in WER.

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Suhaila M. Shakiah, Rupak Vignesh Swaminathan, Hieu Duy Nguyen, Raviteja Chinta, Tariq Afzal, Nathan Susanj, Athanasios Mouchtaris, Grant P. Strimel, Ariya Rastrow. 2023-05-12. Accelerator-Aware Training for Transducer-Based Speech Recognition. https://doi.org/10.1109/slt54892.2023.10022592

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