Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

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

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Abstract Design of hardware accelerators for neural network (NN) applications involves
walking a tight rope amidst the constraints of low-power, high accuracy and throughput …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …

As deep learning inference applications are increasing in embedded devices, an embedded
device tends to equip neural processing units (NPUs) in addition to a multi-core CPU and a …

The recent ground-breaking advances in deep learning networks (DNNs) make them
attractive for embedded systems. However, it can take a long time for DNNs to make an …

The convenience of availing quality services at affordable costs anytime and anywhere
makes mobile technology very popular among users. Due to this popularity, there has been …

CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL)
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …

Deep neural networks (DNNs) are becoming a key enabling technique for many application
domains. However, on-device inference on battery-powered, resource-constrained …