While Strassen’s matrix multiplication algorithm reduces the complexity of naive matrix multiplication, general-purpose hardware is not suitable for achieving the algorithm’s promised theoretical speedups. This leaves the question of whether it could be better exploited in custom hardware architectures designed specifically for executing the algorithm. However, there is limited prior work on this and it is not immediately clear how to design such architectures or whether they can ultimately lead to real improvements. We bridge this gap, presenting and evaluating new systolic array architectures that efficiently translate the theoretical complexity reductions of Strassen’s algorithm directly into hardware resource savings. Furthermore, the architectures are multisystolic array designs that can multiply smaller matrices with higher utilization than single-systolic array designs. The proposed designs implemented on FPGA reduce DSP requirements by a factor of 1.14× for certain targeted Strassen recursive levels, and otherwise require overall similar soft logic resources when instantiated to support matrix sizes down to 32 × 32 and 24 × 24 at one to two levels of Strassen recursion, respectively. We evaluate the proposed designs in both isolation and an end-to-end machine learning accelerator compared with baseline designs and prior works, achieving state-of-the-art performance.
