MatHE: A Near-Mat Processing In-Memory Accelerator for Fully Homomorphic Encryption

Abstract

Fully homomorphic encryption (FHE) enables arbitrary computations on encrypted data without decryption, securing many emerging applications. Unfortunately, FHE computation is orders of magnitude slower than computation on plain data due to the explosion in data size after encryption. We propose a PIM-based FHE accelerator, MatHE, which exploits a novel processing in-memory technology with near-mat processing to achieve high-throughput and efficient acceleration for FHE. Our evaluation shows MatHE achieves 4.0× speedup and 6.9× efficiency improvement over state-of-the-art FHE accelerators.