High demand for computation resources severely hinders deployment of large-scale Deep
Neural Networks (DNN) in resource constrained devices. In this work, we propose a …

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 …

This paper creates a paradigm shift with regard to the way we build neural extractive
summarization systems. Instead of following the commonly used framework of extracting sentences …

Magnetic random access memory (MRAM) is a promising memory technology, which has
fast read access, high density, and non-volatility. Using 3D heterogeneous integrations, it …

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 learning (ML…

Federated learning (FL) has been a popular method to achieve distributed machine learning
among numerous devices without sharing their data to a cloud server. FL aims to learn a …

By mimicking the highly parallel biological systems, neuromorphic hardware provides the
capability of information processing within a compact and energy-efficient platform. However, …

High network communication cost for synchronizing gradients and parameters is the well-known
bottleneck of distributed training. In this work, we propose TernGrad that uses ternary …

An image encryption algorithm based on chaotic system and deoxyribonucleic acid (DNA)
sequence operations is proposed in this paper. First, the plain image is encoded into a DNA …

In recent years, non-volatile memory (NVM) technologies have emerged as candidates for
future universal memory. NVMs generally have advantages such as low leakage power, high …