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arXiv · 2103.01308

SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration

Shurui Li·Wojciech Romaszkan·Alexander Graening·Puneet Gupta
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Abstract

Quantization is spearheading the increase in performance and efficiency of neural network computing systems making headway into commodity hardware. We present SWIS - Shared Weight bIt Sparsity, a quantization framework for efficient neural network inference acceleration delivering improved performance and storage compression through an offline weight decomposition and scheduling algorithm. SWIS can achieve up to 54.3% (19.8%) point accuracy improvement compared to weight truncation when quantizing MobileNet-v2 to 4 (2) bits post-training (with retraining) showing the strength of leveraging shared bit-sparsity in weights. SWIS accelerator gives up to 6x speedup and 1.9x energy improvement overstate of the art bit-serial architectures.

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Shurui Li, Wojciech Romaszkan, Alexander Graening, Puneet Gupta. 2021-03-01. SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration. https://arxiv.org/abs/2103.01308

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