SPRITE: Sparsity-Aware Neural Processing Unit with Constant Probability of Index-Matching

Sparse neural networks are widely used for memory savings. However, irregular indices of non-zero input activations and weights tend to degrade the overall system performance. This paper presents a scheme to maintain constant probability of index-matching for weight and input over a wide range of sparsity overcoming a critical limitation in previous works. A sparsity-aware neural processing unit based on the proposed scheme improves the system performance up to 6.1× compared to previous sparse convolutional neural network hardware accelerators.