NTT-PIM: Row-Centric Architecture and Mapping for Efficient Number-Theoretic Transform on PIM

Abstract

Recently DRAM-based PIMs (processing-in-memories) with unmodified cell arrays have demonstrated impressive performance for accelerating AI applications. However, due to the very restrictive hardware constraints, PIM remains an accelerator for simple functions only. In this paper we propose NTT-PIM, which is based on the same principles such as no modification of cell arrays and very restrictive area budget, but shows state-of-the-art performance for a very complex application such as NTT, thanks to features optimized for the application's characteristics, such as in-place update and pipelining via multiple buffers. Our experimental results demonstrate that our NTT-PIM can outperform previous best PIM-based NTT accelerators in terms of runtime by 1.7 ∼ 17× while having negligible area and power overhead.