Architecture Aware Latency Constrained Sparse Neural Networks

• URL: http://arxiv.org/abs/2109.00170v1
• Date: Wed, 1 Sep 2021 03:41:31 GMT
• Title: Architecture Aware Latency Constrained Sparse Neural Networks
• Authors: Tianli Zhao, Qinghao Hu, Xiangyu He, Weixiang Xu, Jiaxing Wang, Cong Leng, Jian Cheng
• Abstract summary: In this paper, we design an architecture aware latency constrained sparse framework to prune and accelerate CNN models. We also propose a novel sparse convolution algorithm for efficient computation. Our system-algorithm co-design framework can achieve much better frontier among network accuracy and latency on resource-constrained mobile devices.
• Score: 35.50683537052815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acceleration of deep neural networks to meet a specific latency constraint is essential for their deployment on mobile devices. In this paper, we design an architecture aware latency constrained sparse (ALCS) framework to prune and accelerate CNN models. Taking modern mobile computation architectures into consideration, we propose Single Instruction Multiple Data (SIMD)-structured pruning, along with a novel sparse convolution algorithm for efficient computation. Besides, we propose to estimate the run time of sparse models with piece-wise linear interpolation. The whole latency constrained pruning task is formulated as a constrained optimization problem that can be efficiently solved with Alternating Direction Method of Multipliers (ADMM). Extensive experiments show that our system-algorithm co-design framework can achieve much better Pareto frontier among network accuracy and latency on resource-constrained mobile devices.

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