PPIMCE: In-Memory Computing Fabric for Privacy Preserving Computing

Privacy has rapidly become a major concern/design consideration. Homomorphic encryption (HE) and garbled circuits (GC) are privacy-preserving techniques that support computations on encrypted data. HE and GC can complement each other, as HE is more efficient for linear operations, while GC is more effective for non-linear operations. Together, they enable complex computing tasks, such as machine learning, to be performed exactly on ciphertexts. However, HE and GC introduce two major bottlenecks: an elevated computational overhead and high data transfer costs. This paper presents Privacy Preserving In-Memory Computing Engine (PPIMCE), an in-memory computing (IMC) fabric designed to mitigate both computational overhead and data transfer issues. Through the use of multiple IMC cores for high parallelism, and by leveraging in-SRAM IMC for data management, PPIMCE offers a compact, energy-efficient solution for accelerating HE and GC. PPIMCE achieves a 107x speedup against a CPU implementation of GC. Additionally, PPIMCE achieves a 1500x and 800x speedup compared with CPU and GPU implementations of CKKS-based HE multiplications. For privacy-preserving machine learning inference, PPIMCE attains a 1000x speedup compared with CPU and a 12x speedup against CraterLake, the state-of-art privacy preserving computation accelerator.