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Foundations and Trends® in Electronic Design Automation
Journal Prestige (SJR): 0.137

Citation Impact (citeScore): 1
Number of Followers: 1

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ISSN (Print) 1551-3939 - ISSN (Online) 1551-3947
Published by Now Publishers Inc

[28 journals]

From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration,
Simulation, and Frameworks
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  Abstract: Over the past decade, a massive proliferation of machine learning algorithms has emerged, from applications for surveillance to self-driving cars. The turning point occurred with the arrival of Convolutional Neural Network (CNN) models and the incredible accuracy brought by Deep Neural Networks (DNNs) at the cost of high computational complexity. In this growing environment, graphic processing units (GPUs) have become the de facto reference platform for the training and inference phases of CNNs and DNNs due to their high processing parallelism and memory bandwidth. However, GPUs are power-hungry architectures. To enable the deployment of CNN and DNN applications on energy-constrained devices (e.g., IoT devices), industry and academic research have moved towards hardware accelerators. Following the evolution of neural networks (from CNNs to DNNs), this survey sheds light on the impact of this architectural shift and discusses hardware accelerator trends in terms of design, exploration, simulation, and frameworks developed in both academia and industry.Suggested CitationLeonardo Rezende Juracy, Rafael Garibotti and Fernando Gehm Moraes (2023), "From CNN to DNN Hardware Accelerators: A Survey on Design, Exploration, Simulation, and Frameworks", Foundations and Trends® in Electronic Design Automation: Vol. 13: No. 4, pp 270-344. http://dx.doi.org/10.1561/1000000060
  PubDate: Mon, 06 Mar 2023 00:00:00 +010
Self-Powered Wearable IoT Devices for Health and Activity Monitoring
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  Abstract: Wearable devices have the potential to transform multiple facets of human life, including healthcare, activity monitoring, and interaction with computers. However, a number of technical and adaptation challenges hinder the widespread and daily usage of wearable devices. Recent research efforts have focused on identifying these challenges and solving them such that the potential of wearable devices can be realized.This monograph starts with a survey of the recent literature on the challenges faced by wearable devices. Then, it discusses potential solutions to each of the challenges. We start with the primary application areas that provide value to the users of wearable devices. We then present recent work on the design of physically flexible and bendable devices that aim to improve user comfort. We also discuss state-of-the-art energy harvesting and security solutions that can improve user compliance of wearable devices. Overall, this monograph aims to serve as a comprehensive resource for challenges and solutions towards self-powered wearable devices for health and activity monitoring.Suggested CitationGanapati Bhat, Ujjwal Gupta, Yigit Tuncel, Fatih Karabacak, Sule Ozev and Umit Y. Ogras (2020), "Self-Powered Wearable IoT Devices for Health and Activity Monitoring", Foundations and Trends® in Electronic Design Automation: Vol. 13: No. 3, pp 145-269. http://dx.doi.org/10.1561/1000000056
  PubDate: Thu, 19 Nov 2020 00:00:00 +010

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