In automatic speech recognition (ASR), model pruning is a widely adopted
technique that reduces model size and latency to deploy neural network models
on edge devices with resource constraints. However, multiple models with
different sparsity levels usually need to be separately trained and deployed to
heterogeneous target hardware with different resource specifications and for
applications that have various latency requirements. In this paper, we present
Dynamic Sparsity Neural Networks (DSNN) that, once trained, can instantly
switch to any predefined sparsity configuration at run-time. We demonstrate the
effectiveness and flexibility of DSNN using experiments on internal production
datasets with Google Voice Search data, and show that the performance of a DSNN
model is on par with that of individually trained single sparsity networks. Our
trained DSNN model, therefore, can greatly ease the training process and
simplify deployment in diverse scenarios with resource constraints.

本文介绍了动态稀疏神经网络 (DSNN) 技术，该技术能够在训练后根据需要在运行时即时切换到任何预定义的稀疏度配置，有效地解决了自动语音识别 (ASR) 等场景中硬件资源限制和延迟要求不同的问题，实验结果表明，DSNN 模型的表现与单一稀疏度网络的表现相当。