SYSTOLIC SPARSE MATRIX VECTOR MULTIPLY IN THE AGE OF TPUS AND ACCELERATORS

摘要

Tensor Processing Units has brought back systolic arrays as a computational alternative to high performance computing. Recently Google presented a Tensor Processing Unit for handling matrix multiplication using systolic arrays. This unit is designed for dense matrices only. As they stated, sparse architectural support was omitted momentarily but they will focus on sparsity in future designs. We propose a systolic array to compute the Sparse Matrix Vector product in T2(n) ≈「(nnz)/2」+2n+2 using 2n+2 processing elements. The systolic array we propose also use accumulators to collect the partial results of the resulting vector and supports adapting tiling.