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.

翻译：隐私已迅速成为主要关注点和设计考虑因素。全同态加密（HE）和混淆电路（GC）是支持对加密数据进行计算的隐私保护技术。HE和GC可以相互补充，因为HE对线性运算更高效，而GC对非线性运算更有效。二者结合能够使得诸如机器学习等复杂计算任务在密文上精确执行。然而，HE和GC引入了两大瓶颈：高昂的计算开销和高昂的数据传输成本。本文提出PPIMCE——一种旨在缓解计算开销和数据传输问题的内存计算（IMC）架构。通过使用多个IMC核心实现高并行性，并利用静态随机存储器内计算（in-SRAM IMC）进行数据管理，PPIMCE为加速HE和GC提供了一种紧凑、节能的解决方案。与GC的CPU实现相比，PPIMCE实现了107倍的加速。此外，在基于CKKS方案的HE乘法运算中，与CPU和GPU实现相比，PPIMCE分别实现了1500倍和800倍的加速。对于隐私保护机器学习推理，与CPU相比，PPIMCE获得了1000倍的加速，与当前最先进的隐私保护计算加速器CraterLake相比，实现了12倍的加速。