While deep learning has shown superior performance in various intelligent tasks, it is still a
challenging problem to deploy sophisticated models on resource-limited edge devices. Filter …

This work aims to enable on-device training of convolutional neural networks (CNNs) by
reducing the computation cost at training time. CNN models are usually trained on high …

This work is an introduction and a survey for the Special Issue on Machine Intelligence at the
Edge. The authors argue that workloads that were formerly performed in the cloud are …

Resistive random-access memory (ReRAM) is a promising technology for designing
hardware accelerators for deep neural network (DNN) inferencing. However, stochastic …

Recently, there has been an explosive growth of mobile and embedded applications using
convolutional neural networks (CNNs). To alleviate their excessive computational demands …

The increasing computational cost of deep neural network models limits the applicability of
intelligent applications on resource-constrained edge devices. While a number of neural …

Autonomous Design Space Exploration of Computing Systems for Sustainability:
Opportunities and Challenges Page 1 35 2168-2356/19©2019 IEEE Copublished by the …

Wireless charging has been demonstrated as a promising technology for prolonging device
operational lifetimes in Wireless Rechargeable Networks (WRNs). To schedule a mobile …

Wearable systems require resource-constrained embedded devices for the elaboration of
the sensed data. These devices have to host energy-efficient artificial intelligence (AI) …

While deep neural networks have achieved superior performance in a variety of intelligent
applications, the increasing computational complexity makes them difficult to be deployed …