Error-aware probabilistic training for memristive neural networks.

ABSTRACT: Analog computing-in-memory devices leverage fundamental physical laws for computation, greatly enhancing energy efficiency. However, the stochastic characteristics of analog devices conflict with the deterministic weight update of the backpropagation algorithm (BP), limiting training performance. To overcome the algorithm-device mismatch, we propose an error-aware probabilistic update method (EaPU) that updates the weights based on a specified probability derived from device writing noise. Compared to BP, EaPU reduces the number of weight updates to <1‰ with minimal performance loss. Furthermore, we validate EaPU experimentally on a 180 nm memristor system for image denoising and super-resolution and simulate its performance on ResNet and Vision Transformers. Results confirm that EaPU trai

SUBMITTER: Liu J 

PROVIDER: S-EPMC12749908 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature