2026/07/20 by Kwunhang Wong, Jichang Yang, Karl M. H. Lai +7
Computer Science · Engineering · #Advanced Memory and Neural Computing #Ferroelectric and Negative Capacitance Devices #Privacy-Preserving Technologies in Data #cs.CR #cs.ET
paper · pdf · doi:10.1145/3831252.3834125
International Conference on Computer-Aided Design 2026
openalex publication_date 2026/07/20 · openalex created_date 2026/07/30 · openalex updated_date 2026/07/30 · arxiv created 2026/07/31 · arxiv updated 2026/08/03
Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory (RRAM) is an attractive substrate for efficient AIoT thanks to its multi-bit storage and compute-in-memory (CiM) capabilities, while its inherently stochastic write behavior provides a natural source of randomness that can be leveraged for differential privacy (DP) protection. Yet how to transform this device-level randomness-typically viewed as detrimental to accuracy-into a principled randomized mechanism while preserving model utility remains underexplored. We propose RRAM-DP, a hardware-algorithm co-design that relaxes RRAM write-verify operations to inject calibrated noise for inherently (epsilon, delta)-DP with formal DP analysis; together with pretraining techniques, it renders a novel private, high-utility CiM training paradigm. On CIFAR-10/100, STS-B, and SST-2, RRAM-DP-SGD incurs at best only a 3.8% accuracy drop at (epsilon=2, delta=O(1/n))-DP relative to non-private SGD. At the same privacy level, RRAM-DP-SGD delivers up to 57x and 3.2x energy savings and 2.7x and 1.8x speedups over A100 and DiVa-GEMM, respectively. These results point toward efficient, privacy-preserving in-memory training on RRAM at the edge.