The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …

Hepatocellular carcinoma (HCC) is a form of liver cancer that is widespread in Europe,
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …

As the use of deep neural networks continues to grow, so does the fraction of compute
cycles devoted to their execution. This has led the CAD and architecture communities to …

This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …

Deep neural networks (DNNs) have been shown to tolerate" brain damage": cumulative
changes to the network's parameters (eg, pruning, numerical perturbations) typically result in …

Graphics processing units (GPUs) are playing a critical role in convolutional neural networks
(CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments …

The effectiveness of deep neural networks (DNN) in vision, speech, and language
processing has prompted a tremendous demand for energy-efficient high-performance DNN …

Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …

The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as
exemplified by several headline-making accidents. While AV development today involves …