Structured Transforms for Small-Footprint Deep Learning

Vikas Sindhwani; Tara N. Sainath; Sanjiv Kumar

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arXiv · 1510.01722

Structured Transforms for Small-Footprint Deep Learning

Vikas Sindhwani·Tara N. Sainath·Sanjiv Kumar

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Abstract

We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile devices. We propose a unified framework to learn a broad family of structured parameter matrices that are characterized by the notion of low displacement rank. Our structured transforms admit fast function and gradient evaluation, and span a rich range of parameter sharing configurations whose statistical modeling capacity can be explicitly tuned along a continuum from structured to unstructured. Experimental results show that these transforms can significantly accelerate inference and forward/backward passes during training, and offer superior accuracy-compactness-speed tradeoffs in comparison to a number of existing techniques. In keyword spotting applications in mobile speech recognition, our methods are much more effective than standard linear low-rank bottleneck layers and nearly retain the performance of state of the art models, while providing more than 3.5-fold compression.

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BibTeX RIS

Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar. 2015-10-06. Structured Transforms for Small-Footprint Deep Learning. https://arxiv.org/abs/1510.01722

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