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, in order to optimize for hardware with different resource specifications and for applications that have various latency requirements , models with varying sparsity levels usually need to be trained and deployed separately . In this paper, generalizing from slimmable neural networks, we present dynamic sparsity neural networks (DSNN) that, once trained, can instantly switch to execute at any given sparsity level at run-time. We show the efficacy of such models on ASR through comprehensive experiments and demonstrate that the performance of a dynamic sparsity model is on par with , and in some cases exceeds, the performance of individually trained single sparsity networks. A trained DSNN model can therefore greatly ease the training process and simplifies deployment in diverse scenarios with resource constraints.
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.
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, models with varying sparsity levels usually need to be trained and deployed separately
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generalizing from slimmable neural networks, we present dynamic sparsity neural networks
we present Dynamic Sparsity Neural Networks
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execute at any given sparsity level
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show the efficacy of such models on ASR through comprehensive experiments and demonstrate
demonstrate the effectiveness and flexibility of DSNN using experiments on internal production datasets with Google Voice Search data, and show
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