An Efficient Sparse-Aware Summation Optimization Strategy for DNN Accelerator

Due to the various applications and high sparsity of deep neural network (DNN), a lot of sparse-aware DNN accelerators have been proposed to exploit the sparsity in DNN. Furthermore, it is essential to optimize for the accumulations and inter-channel aggregations in DNN to reduce memory overhead and improve performance of DNN accelerator. However, the uncertain number and location of non-zero element in DNN pose critical challenges for optimizing such accelerators and this inspires us to explore an efficient spare-aware summation optimization strategy for DNN accelerator. In this paper, we leverage the strategy that trading higher cost memory storage/access for lower cost computation to propose a random index based sparse-aware adder tree (RAT), which achieves a better trade-off among performance, hardware resource overhead and adaptability. Synthesis and simulation results demonstrate that, compared with reference design, the proposed design achieves 1.71× and 1.52× the normalized area efficiency and energy efficiency improvement on ResNet18, respectively.