Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …

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 …

Binary instrumentation frameworks are widely used to implement profilers, performance
evaluation, error checking, and bug detection tools. While dynamic binary instrumentation …

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 …

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

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 …

PyTorchFI is a runtime perturbation tool for deep neural networks (DNNs), implemented for
the popular PyTorch deep learning platform. PyTorchFI enables users to perform …

GPUs have found wide acceptance in domains such as high-performance computing and
autonomous vehicles, which require fast processing of large amounts of data along with …

Machine learning is among the greatest advancements in computer science and
engineering and is today used to classify or detect objects, a key feature in autonomous …