Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

Challenges and Opportunities in Near-Threshold DNN Accelerators around Timing Errors

AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design paradigms are inevitable to realize the complete potential of AI evolution and curtail energy consumption. The Near-Threshold Computing (NTC) design paradigm can serve as the best candidate for providing the required energy efficiency. However, NTC operation is plagued with ample performance and reliability concerns arising from the timing errors. In this paper, we dive deep into DNN architecture to uncover some unique challenges and opportunities for operation in the NTC paradigm. By performing rigorous simulations in TPU systolic array, we reveal the severity of timing errors and its impact on inference accuracy at NTC. We analyze various attributes—such as data–delay relationship, delay disparity within arithmetic units, utilization pattern, hardware homogeneity, workload characteristics—and uncover unique localized and global techniques to deal with the timing errors in NTC

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

[Abstract] Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

High-Density Solid-State Memory Devices and Technologies

This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms

Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing