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Inference efficiency is the predominant consideration in designing deeplearning accelerators. Previous work mainly focuses on skipping zero values todeal with remarkable ineffectual computation, while zero bits in non-zerovalues, as another major source of ineffectual computation, is often ignored.The reason lies on the difficulty of extracting essential bits during operatingmultiply-and-accumulate (MAC) in the processing element. Based on the fact thatzero bits occupy as high as 68.9% fraction in the overall weights of moderndeep convolutional neural network models, this paper firstly proposes a weightkneading technique that could eliminate ineffectual computation caused byeither zero value weights or zero bits in non-zero weights, simultaneously.Besides, a split-and-accumulate (SAC) computing pattern in replacement ofconventional MAC, as well as the corresponding hardware accelerator designcalled Tetris are proposed to support weight kneading at the hardware level.Experimental results prove that Tetris could speed up inference up to 1.50x,and improve power efficiency up to 5.33x compared with the state-of-the-artbaselines.

Tetris: Re-architecting Convolutional Neural Network Computation for Machine Learning Accelerators