Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …

Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge
devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory …

Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

To tackle important combinatorial optimization problems, a variety of annealing-inspired
computing accelerators, based on several different technology platforms, have been …

Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Recent progress in artificial intelligence is largely attributed to the rapid development of
machine learning, especially in the algorithm and neural network models. However, it is the …

Convolutional neural networks (CNNs) are emerging as powerful tools for image
processing. Recent machine learning work has reduced CNNs' compute and data volumes …