Recently, due to the availability of big data and the rapid growth of computing power,
artificial intelligence (AI) has regained tremendous attention and investment. Machine …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Convolution neural networks (CNNs) are the heart of deep learning applications. Recent
works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access …

A number of recent efforts have attempted to design accelerators for popular machine
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

In-memory computing is a promising approach to addressing the processor-memory data
transfer bottleneck in computing systems. We propose spin-transfer torque compute-in …

Graph processing recently received intensive interests in light of a wide range of needs to
understand relationships. It is well-known for the poor locality and high memory bandwidth …

Deep Neural Networks (DNNs) have demonstrated state-of-the-art performance on a broad
range of tasks involving natural language, speech, image, and video processing, and are …