Privacy Preserving In-memory Computing Engine
Haoran Geng, Jianqiao Mo, Dayane Reis, Jonathan Takeshita, Taeho Jung,, Brandon Reagen, Michael Niemier, Xiaobo Sharon Hu

TL;DR
PPIMCE is an in-memory computing fabric that significantly accelerates privacy-preserving computations like homomorphic encryption and garbled circuits, reducing overhead and enabling efficient privacy-preserving machine learning inference.
Contribution
The paper introduces PPIMCE, a novel in-memory computing architecture that mitigates computational and data transfer bottlenecks in privacy-preserving cryptographic techniques.
Findings
Achieves 107X speedup over CPU for garbled circuits.
Attains 1,500X and 800X speedup over CPU and GPU for HE multiplications.
Provides 1,000X speedup in privacy-preserving ML inference compared to CPU.
Abstract
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.…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Cryptography and Data Security
