ExTensor 论文

摘要

Generalized tensor algebra is a prime candidate for acceleration via customized ASICs. Modern tensors feature a wide range of data sparsity, with the density of non-zero elements ranging from 10-6% to 50%. This paper proposes a novel approach to accelerate tensor kernels based on the principle of hierarchical elimination of computation in the presence of sparsity. This approach relies on rapidly finding intersections---situations where both operands of a multiplication are non-zero---enabling new data fetching mechanisms and avoiding memory latency overheads associated with sparse kernels implemented in software.