Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …

Processing in memory (PIM) architecture, with its ability to perform ultra-low-latency parallel
processing, is regarded as a more suitable alternative to von Neumann computing …

Reconfigurable nanotechnologies such as Silicon Nanowire Field Effect Transistors (FETs)
serve as a promising technology that not only facilitates lower power consumption but also …

Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-
linear because of the growing demand on prediction accuracy and analysis quality. The …

Emerging applications including deep neural networks (DNNs) and convolutional neural
networks (CNNs) employ massive amounts of data to perform computations and data …

Emerging applications reliant on deep neural networks (DNNs) and convolutional neural
networks (CNNs) demand substantial data for computation and analysis. Deploying DNNs …

Weight pruning is an effective model compression technique to tackle the challenges of
achieving real-time deep neural network (DNN) inference on mobile devices. However, prior …

System on chips (SoCs) are now designed with their own artificial intelligence (AI)
accelerator segment to accommodate the ever-increasing demand of deep learning (DL) …

Abstract Machine learning has become increasingly popular in recent years. Due to the high
accuracy and excellent scalability, deep neural networks have emerged as a fundamental …