High accuracy and ever-increasing computing power have made deep neural networks
(DNNs) the algorithm of choice for various machine learning, computer vision, and image …

Due to their growing popularity and computational cost, deep neural networks (DNNs) are
being targeted for hardware acceleration. A popular architecture for DNN acceleration …

As convolutional neural network CNN accelerators are being adopted in emerging safety-
critical areas, their reliability becomes prominent. The systolic array is widely used as the …

Editor's note: Systolic array is embracing its renaissance after being accepted by Google
TPU as the core computing architecture of machine learning acceleration. In this article, the …

With the advent of Deep Neural Network (DNN) accelerators, permanent faults are
increasingly becoming a serious challenge for DNN hardware accelerator, as they can …

The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability
stand along with the need for reducing the computational burden on the hardware platforms …

With data-driven analytics becoming mainstream, the global demand for dedicated artificial
intelligence (AI) and deep learning accelerator chips is soaring. These accelerators …

Machine Learning (ML) is making a strong resurgence in tune with the massive generation
of unstructured data which in turn requires massive computational resources. Due to the …

As an energy-efficient hardware solution for deep neural network (DNN) inference, systolic
accelerators are particularly popular in both embedded and datacenter computing …

Prior works typically conducted the fault analysis of neural network accelerator computing
arrays with simulation and focused on the prediction accuracy loss of the neural network …