The efficiency of machine intelligence techniques has improved noticeably in the embedded
application domains thanks to the dedicated hardware accelerators for deep neural …

The growing popularity of convolutional neural networks (CNNs) has led to the search for
efficient computational platforms to accelerate CNN training. Resistive random-access …

Owing to the inherent fault tolerance of deep neural network (DNN) models used for
classification, many structural faults in the processing elements (PEs) of a systolic array …

The application diversity and evolution of AI accelerator architectures require innovative DFT
solutions to address issues such as test time, test power, performance and area overhead …

Deep learning (DL) applications are becoming in-creasingly ubiquitous. However, recent
research has highlighted a number of reliability concerns associated with deep neural …

As the demand for data-intensive analytics has increased with the rapid advance in artificial
intelligence (AI), various AI accelerators have been proposed. However, as AI-based …

In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have
provided a promising solution to the acceleration of neural networks. However, stuck-at …

The ubiquitous application of deep neural networks (DNN) has led to a rise in demand for AI
accelerators. DNN-specific functional criticality analysis identifies faults that cause …

We present a topological and probabilistic frame-work to estimate the functional criticality of
defects in an AI inferencing accelerator. From the application workload, we extract the …

Recent advances in deep learning can be attributed to the continued performance
improvement of hardware processors and artificial intelligence (AI) accelerators. In addition …