Layer ensemble averaging for fault tolerance in memristive neural networks.

ABSTRACT: Artificial neural networks have advanced due to scaling dimensions, but conventional computing struggles with inefficiencies due to memory bottlenecks. In-memory computing architectures using memristor devices offer promise but face challenges due to hardware non-idealities. This work proposes layer ensemble averaging-a hardware-oriented fault tolerance scheme for improving inference performance of non-ideal memristive neural networks programmed with pre-trained solutions. Simulations on an image classification task and hardware experiments on a continual learning problem with a custom 20,000-device prototyping platform show significant performance gains, outperforming prior methods at similar redundancy levels and overheads. For the image classification task with 20% stuck-at faults, accu

SUBMITTER: Yousuf O 

PROVIDER: S-EPMC11787353 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature