The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …

An energy-efficient deep neural network (DNN) accelerator, unified neural processing unit
(UNPU), is proposed for mobile deep learning applications. The UNPU can support both …

Large-scale matrix-vector multiplications, which dominate in deep neural networks (DNNs),
are limited by data movement in modern VLSI technologies. This paper addresses data …

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the
size of DNNs continues to grow, it is critical to improve the energy efficiency and …

Model compression is an important technique to facilitate efficient embedded and hardware
implementations of deep neural networks (DNNs), a number of prior works are dedicated to …

This paper describes a novel approach of packing sparse convolutional neural networks into
a denser format for efficient implementations using systolic arrays. By combining multiple …

DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs
during the DNN computations to reduce data movement from DRAM to the chip. The reuse is …

In the realm of surface electromyography (sEMG) gesture recognition, deep learning
algorithms are seldom employed. This is due in part to the large quantity of data required for …

Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …