The expeditious development of information technology has led to the rise of artificial
intelligence (AI). However, conventional computing systems are prone to volatility, high …

Recent years have witnessed a significant interest in the “generative adversarial
networks”(GANs) due to their ability to generate high-fidelity data. Many models of GANs …

In this paper, we propose an efficient convolutional neural network (CNN) accelerator
design, entitled RNSiM, based on the Residue Number System (RNS) as an alternative for …

Approximate computing aims to reduce the power consumption and design complexity of
digital systems with the cost of a tolerable error. In this article, two ultra-efficient magnetic …

In recent years, there have been significant advancements in the manufacturing of emerging
technologies, especially in the areas of in-memory computing and neural networks, which …

Deep neural networks (DNNs) have shown their great capability of surpassing human
performance in many areas. With the help of quantization, artificial intelligence (AI) powered …

Recently, the Logic-in-Memory (LiM) concept has been widely studied in the literature. This
paradigm represents one of the most efficient ways to solve the limitations of a Von …

Unlabeled data processing is of great significance for artificial intelligence (AI), since well-
structured labeled data are scarce in a majority of practical applications due to the high cost …

Generative adversarial networks (GANs) consist of multiple deep neural networks
cooperating and competing with each other. Due to their complex architectures and large …

Since its invention, generative adversarial networks (GANs) have shown outstanding results
in many applications. GANs are powerful, yet resource-hungry deep learning models. The …