Are Emerging Machine Learning Models Dependable at the Nanoscales?

Machine learning models by emerging neural networks (NNs) are being developed to solve advanced and complex tasks; for example, multi-branch NNs (such as Siamese and Triplet networks) have been proposed to perform similarity checking tasks, and large-scale models (such as Bidirectional Encoder Representations from Transformers (BERT) and Stable Diffusion) have been developed for nature language and image processing. These emerging NNs that often have a large model size, are potentially resilient to small errors during computation; however, when considering random errors originating from the underlying hardware implementation (such as soft errors on nanoscale circuits induced by radiation or voltage scaling), their complexity poses an obvious question: can these models still operate dependably when implemented by nano-scaled CMOS technologies? This article explores this concern by analyzing and evaluating the dependability of emerging NNs in the presence of hardware induced errors; the results are expected to provide insights for relevant error protection techniques when applying these models in safety-critical applications.