  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 times while having negligible area and power overhead.
