Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

In the modern-day era of technology, a paradigm shift has been witnessed in the areas
involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …

The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …

Deep neural networks (DNNs) have become an essential tool in artificial intelligence, with a
wide range of applications such as computer vision, medical diagnosis, security, robotics …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

The main goal of the field of neuromorphic computing is to build machines that emulate
aspects of the brain in its ability to perform complex tasks in parallel and with great energy …

Integrated photonic circuits are created as a stable and small form factor analogue of fiber-
based optical systems, from wavelength-division multiplication transceivers to more recent …

Recent developments in deep neural network (DNN) pruning introduces data sparsity to
enable deep learning applications to run more efficiently on resourceand energy …