RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning
A new preprint introduces RRAM-DP, a hardware-algorithm co-design that utilizes the inherent stochastic write behavior of resistive-switching random-access memory (RRAM) devices to inject calibrated noise for differential privacy in edge AIoT systems. The approach achieves at most a 3.8% accuracy drop at (ε=2, δ=O(1/n))-DP on benchmarks such as CIFAR-10/100, and demonstrates up to 57x energy savings and 2.7x speedups compared to GPU baselines. This method represents a novel use of device-level randomness for privacy-preserving, efficient in-memory training.
Why it matters: This work offers a significant advance in privacy-preserving machine learning for edge devices by leveraging physical properties of emerging memory technology, potentially enabling more secure and efficient AIoT applications.
Full story at: arXiv Cryptography and Security ↗