Path-based Processing using In-Memory Systolic Arrays for Accelerating Data-Intensive Applications

The next wave of scientific discovery is predicated on unleashing beyond-exascale simulation capabilities using in-memory computing. Path-based computing is a promising in-memory logic style for accelerating Boolean logic with deterministic precision. However, existing studies on path-based computing are limited to executing small combinational circuits. In this paper, we propose a framework called PSYS to accelerate data-intensive scientific computing applications using path-based in-memory systolic arrays. The approach leverages path-based computing for multiplying known constants with an unknown operand, which substantially reduces the computational complexity compared with general purpose multiplication of two unknown operands. The systolic arrays minimize data movement by storing the matrix elements using non-volatile memory and performing processing in-place. The framework decomposes unstructured computations to the systolic arrays while considering the non-regular computational patterns of the applications. Our experimental evaluations employ applications from the domains of engineering, physics, and mathematics. The experimental results demonstrate that compared with the state-of-the-art, the PSYS framework improves energy and latency by a factor of 101x and 23x, respectively.