  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 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 1,500X and 800X speedup compared to CPU and GPU implementations of
CKKS-based HE multiplications. For privacy-preserving machine learning
inference, PPIMCE attains a 1,000X speedup compared to CPU and a 12X speedup
against CraterLake, the state-of-art privacy preserving computation
accelerator.
