INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices
This work addresses the problem of deploying ASR models on mobile devices, offering an incremental improvement through quantization and fast convolution techniques.
The paper tackles the intensive computation of Automatic Speech Recognition (ASR) models for mobile deployment by proposing a quantized Winograd optimization pipeline, achieving a 1.48x speedup with only a 0.07% word error rate (WER) decrease on ARMv7-based devices.
The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices. In this paper, we present a novel quantized Winograd optimization pipeline, which combines the quantization and fast convolution to achieve efficient inference acceleration on mobile devices for ASR models. To avoid the information loss due to the combination of quantization and Winograd convolution, a Range-Scaled Quantization (RSQ) training method is proposed to expand the quantized numerical range and to distill knowledge from high-precision values. Moreover, an improved Conv1D equipped DFSMN (ConvDFSMN) model is designed for mobile deployment. We conduct extensive experiments on both ConvDFSMN and Wav2letter models. Results demonstrate the models can be effectively optimized with the proposed pipeline. Especially, Wav2letter achieves 1.48* speedup with an approximate 0.07% WER decrease on ARMv7-based mobile devices.