MBSNTT: A Highly Parallel Digital In-Memory Bit-Serial Number Theoretic Transform Accelerator
Abstract
Conventional cryptographic systems protect the data security during communication but give third-party cloud operators complete access to compute decrypted user data. Homomorphic encryption (HE) promises to rectify this and allow computations on encrypted data to be done without actually decrypting it. However, HE encryption requires several orders of magnitude higher latency than conventional encryption schemes. Number theoretic transform (NTT), a polynomial multiplication algorithm, is the bottleneck function in HE. In traditional architectures, memory accesses and support for parallel operations limit NTT’s throughput and energy efficiency. Processing in memory (PIM) is an interesting approach that can maximize parallelism with high-energy efficiency. To enable HE on resource-constrained edge devices, this article presents MBSNTT, a digital in-memory Multi-Bit-Serial NTT accelerator, achieving high parallelism and energy efficiency for NTT with minimized area. MBSNTT features a novel multi-bit-serial modular multiplication algorithm and PIM implementation that computes all modular multiplications in an NTT in parallel. It further adopts a constant geometry NTT data flow for efficient transition between NTT stages and different cores. Our evaluation shows that MBSNTT achieves 1.62× ( 19.08× ) higher throughput and 64.9× ( 2.06× ) lower energy than state-of-the-art PIM NTT accelerators Crypto-PIM (MeNTT), at a polynomial order of 8 K and bit width of 128.
Akhil Pakala
MECE 2024, now at Apple
Zhiyu Chen
PhD 2023, now at Apple
