DAT: Leveraging Device-Specific Noise for Efficient and Robust AI Training in ReRAM-based Systems

Authors : Chanwoo Park, Jongwook Jeon, Hyunbo Cho

Publication : SISPAD

Date : September 2023

The increasing interest in artificial intelligence (AI) and the limitations of general-purpose graphics processing units(GPUs) have prompted the exploration of neuromorphic devices, such as resistive random-access memory (ReRAM), for AI computation. However, ReRAM devices exhibit various sources of variability that impact their performance and reliability. In this paper, we propose Device-Aware Training (DAT), a robust training method that accounts for device-specific noise and resilience against inherent variability in ReRAM devices. To address the significant computational costs of noise-robust training, DAT employs sharpness-aware minimization and a low-rank approximation of the device-specific noise covariance matrix. This leads to efficient computation and reduced training time while maintaining versatility across various model architectures and tasks. We evaluate our method on CIFAR-10 and CIFAR-100 datasets, achieving a 38.2% increase in test accuracy in the presence of analog noise and a 5.9x faster training time compared to using a full-rank covariance matrix. From a loss landscape perspective, we provide insights into addressing noise-induced challenges in the weight space. DAT contributes to the development of reliable and high-performing neuromorphic AI systems based on ReRAM technology. Index Terms—Resistive random-access memory (ReRAM), Robust training, Neuromorphic AI systems

FlowSim: An Invertible Generative Network for Efficient Statistical Analysis under Process Variations

Authors : Chanwoo Park, Hong Chul Nam, Jihun Park, Jongwook Jeon

Publication : SISPAD

Date : September 2023

The analysis of statistical variation in circuits or devices, resulting from process, voltage, and temperature (PVT) variations, is a critical aspect of ensuring high yield and accurate high-sigma analysis in semiconductor fabrication. As the industry progresses toward nanometer technologies, process variation becomes a significant challenge, necessitating the development of effective statistical models. Traditional Monte Carlo simulations, however, struggle to scale with the increasing number of process variables, leading to an exponential growth in the required simulations. In response to this challenge, we introduce FlowSim, a novel approach that employs density estimation to accurately perform yield and high-sigma analysis with a significantly reduced number of simulations. This approach offers a unique solution to the scalability issues faced by conventional Monte Carlo simulations, providing over a 100x decrease in the number of required simulations while maintaining a prediction error below 5% across all statistical metrics of circuit performance..