PPIMCE:In-Memory Computing Fabric for Privacy Preserving ComputingOA

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 computation 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-the-art privacy preserving computation accelerator.