Rethinking Differentiable Search for Mixed-Precision Neural Networks

• URL: http://arxiv.org/abs/2004.05795v1
• Date: Mon, 13 Apr 2020 07:02:23 GMT
• Title: Rethinking Differentiable Search for Mixed-Precision Neural Networks
• Authors: Zhaowei Cai and Nuno Vasconcelos
• Abstract summary: Low-precision networks with weights and activations quantized to low bit-width are widely used to accelerate inference on edge devices. Current solutions are uniform, using identical bit-width for all filters. This fails to account for the different sensitivities of different filters and is suboptimal. Mixed-precision networks address this problem, by tuning the bit-width to individual filter requirements.
• Score: 83.55785779504868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-precision networks, with weights and activations quantized to low bit-width, are widely used to accelerate inference on edge devices. However, current solutions are uniform, using identical bit-width for all filters. This fails to account for the different sensitivities of different filters and is suboptimal. Mixed-precision networks address this problem, by tuning the bit-width to individual filter requirements. In this work, the problem of optimal mixed-precision network search (MPS) is considered. To circumvent its difficulties of discrete search space and combinatorial optimization, a new differentiable search architecture is proposed, with several novel contributions to advance the efficiency by leveraging the unique properties of the MPS problem. The resulting Efficient differentiable MIxed-Precision network Search (EdMIPS) method is effective at finding the optimal bit allocation for multiple popular networks, and can search a large model, e.g. Inception-V3, directly on ImageNet without proxy task in a reasonable amount of time. The learned mixed-precision networks significantly outperform their uniform counterparts.

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