Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models.

ABSTRACT: Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation. To address this challenge, we propose RePACK, a threefold data protection scheme that safeguards neural network input, weight, and structural information. It utilizes a bipartite-sort coding scheme to store data with a fully on-chip physical unclonable function. Experimental results demonstrate the effectiveness of increasing enumeration complexity to 5.77 × 10⁷⁵ for a 128-column compute-in-memory core. We further implement and evaluate a

SUBMITTER: Yue W 

PROVIDER: S-EPMC11762733 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature