MeNTT: A Compact and Efficient Processing-in-Memory Number Theoretic Transform (NTT) Accelerator


Lattice-based cryptography (LBC) exploiting Learning with Errors (LWE) problems is a promising candidate for post-quantum cryptography. Number theoretic transform (NTT) is the latency- and energy- dominant process in the computation of LWE problems. This paper presents a compact and efficient in-MEmory NTT accelerator, named MeNTT, which explores optimized computation in and near a 6T SRAM array. Specifically-designed peripherals enable fast and efficient modular operations. Moreover, a novel mapping strategy reduces the data flow between NTT stages into a unique pattern, which greatly simplifies the routing among processing units (i.e. SRAM column in this work), reducing energy and area overheads. The accelerator achieves significant latency and energy reductions over prior arts.

IEEE Transactions on Very Large Scale Integration Systems (TVLSI)
Dai Li
PhD 2021, now at Google
Akhil Pakala
Ph.D. Student (started in 2020)
Kaiyuan Yang
Associate Professor of ECE