A systematic DNN weight pruning framework using alternating direction method of multipliers

[ArXiv]

Abstract

In this paper, we present a systematic weight pruning framework of DNNs using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning. By using ADMM, the original nonconvex optimization problem is decomposed into two subproblems that are solved iteratively. One of these subproblems can be solved using stochastic gradient descent, while the other can be solved analytically. The proposed ADMM weight pruning method incurs no additional suboptimality besides that resulting from the nonconvex nature of the original optimization problem. Furthermore, our approach achieves a fast convergence rate.