Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks

• URL: http://arxiv.org/abs/2203.14768v1
• Date: Mon, 28 Mar 2022 14:03:16 GMT
• Title: Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
• Authors: Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari, Francesco Conti, Lorenzo Lamberti, Enrico Macii, Luca Benini, Massimo Poncino
• Abstract summary: Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. We propose an automatic dilation, which tackles the problem as a weight pruning on the time-axis, and learns dilation factors together with weights, in a single training.
• Score: 20.943095081056857
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. One key feature of TCNs is time-dilated convolution, whose optimization requires extensive experimentation. We propose an automatic dilation optimizer, which tackles the problem as a weight pruning on the time-axis, and learns dilation factors together with weights, in a single training. Our method reduces the model size and inference latency on a real SoC hardware target by up to 7.4x and 3x, respectively with no accuracy drop compared to a network without dilation. It also yields a rich set of Pareto-optimal TCNs starting from a single model, outperforming hand-designed solutions in both size and accuracy.

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