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Quantization is spearheading the increase in performance and efficiency ofneural network computing systems making headway into commodity hardware. Wepresent SWIS - Shared Weight bIt Sparsity, a quantization framework forefficient neural network inference acceleration delivering improved performanceand storage compression through an offline weight decomposition and schedulingalgorithm. SWIS can achieve up to 54.3% (19.8%) point accuracy improvementcompared to weight truncation when quantizing MobileNet-v2 to 4 (2) bitspost-training (with retraining) showing the strength of leveraging sharedbit-sparsity in weights. SWIS accelerator gives up to 6x speedup and 1.9xenergy improvement overstate of the art bit-serial architectures.

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