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Vehicular traffic has evolved as a crucial means for the transport of persons and goods, and its importance for the economic and social life of modern society cannot be overemphasized. On the other hand, recurrent vehicular traffic congestion, which appears on a daily basis, particularly in metropolitan areas, around the globe, has been a (increasingly) serious, in fact threatening, problem that calls

In this month’s Point-of-View article, the authors propose a novel paradigm for vehicular traffic in the era of connected and automated vehicles (CAVs), which includes two combined principles: lane-free traffic and vehicle nudging; the latter implying that vehicles may be “pushing” from a distance (using communication or sensors) other vehicles in front of them. This traffic paradigm features several

Index modulation (IM) provides a novel way for the transmission of additional data bits via the indices of the available transmit entities compared with classical communication schemes. This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits

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Global connectivity is at an all-time high, and more users than ever before are participating in the online ecosystem. Despite this exciting phenomenon, opportunities to access the online world are not shared equally among the global population. Socioeconomic, political, and geographic factors all play roles in determining the extent to which one can be an online participant [1] . This resultant digital

Point of View: Bridging the Digital Divide: Success Depends on Content Provider and Application Developer Involvement by A. Lappalainen and C. Rosenberg Users in isolated, remote communities who rely on satellites to connect to the Internet are limited by long-latency and low-bandwidth connections, and their quality of experience lags far behind others, creating a digital divide. In this month’s opinion

Arc lighting powered from central stations preceded the better-known incandescent electric lighting powered from central stations and thrived well into the 20th century. After its introduction in 1880, incandescent electric lighting began to displace gas lighting in small indoor environments, but it failed to make inroads into arc lighting for streetlights and large indoor areas before high-intensity

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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This special issue explores the technological contributions and developments of approximate computing at disparate levels and provides insight into exciting directions for the future.

This article presents an overview of circuit-level techniques used for approximate computing (AC), including both computation and data storage units. After providing some background concept and methodology review, this article proceeds to provide a detailed review of prior art in circuit-level approximation techniques for data path and memory. The focus is on identifying key circuit-level approximation

Approximate computing is an advanced computational technique that trades the accuracy of computation results for better utilization of system resources. It has emerged as a new preferable paradigm over traditional computing architectures for many applications where inaccurate results are acceptable. However, approximate computing also introduces security vulnerabilities mainly due to the fact that

Advances in deep neural networks (DNNs) and the availability of massive real-world data have enabled superhuman levels of accuracy on many AI tasks and ushered the explosive growth of AI workloads across the spectrum of computing devices. However, their superior accuracy comes at a high computational cost, which necessitates approaches beyond traditional computing paradigms to improve their operational

This article provides an overview of recently proposed deep in-memory architectures (DIMAs) in SRAM for energy- and latency-efficient hardware realization of machine learning (ML) algorithms. DIMA tackles the data movement problem in von Neumann architectures head-on by deeply embedding mixed-signal computations into a conventional memory array. In doing so, it trades off its computational signal-to-noise

This month’s history article focuses on the story behind the technological breakthrough that led to the world’s first commercial telephones.

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This special issue provides a comprehensive overview of modern optimization tools and methods for the purposes of data-driven learning and control.

Decentralized optimization to minimize a finite sum of functions, distributed over a network of nodes, has been a significant area within control and signal-processing research due to its natural relevance to optimal control and signal estimation problems. More recently, the emergence of sophisticated computing and large-scale data science needs have led to a resurgence of activity in this area. In

Large-scale data science trains models for data sets containing massive numbers of samples. Training is often formulated as the solution of empirical risk minimization problems that are optimization programs whose complexity scales with the number of elements in the data set. Stochastic optimization methods overcome this challenge, but they come with their own set of limitations. This article discusses

There has been an increasing necessity for scalable optimization methods, especially due to the explosion in the size of data sets and model complexity in modern machine learning applications. Scalable solvers often distribute the computation over a network of processing units. For simple algorithms, such as gradient descent, the dependence of the convergence time with the topology of this network

Recent advances in sensing, communication, and computing technologies have enabled the use of multirobot systems for practical applications such as surveillance, area mapping, and search and rescue. For such systems, a major challenge is to design decision rules that are real-time-implementable, require local information only, and guarantee some desired global performance. Distributed optimization

Stochastic optimization lies at the heart of machine learning, and its cornerstone is stochastic gradient descent ( SGD ), a method introduced over 60 years ago. The last eight years have seen an exciting new development: variance reduction for stochastic optimization methods. These variance-reduced ( VR ) methods excel in settings where more than one pass through the training data is allowed, achieving

Emerging applications of machine learning in numerous areas—including online social networks, remote sensing, Internet-of-Things (IoT) systems, smart grids, and more—involve continuous gathering of and learning from streams of data samples. Real-time incorporation of streaming data into the learned machine learning models is essential for improved inference in these applications. Furthermore, these

Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational

Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There is a large body of literature examining the effect of the graph structure on the performance of decentralized processing strategies. In this article

On March 7, 1876, Alexander Graham Bell was granted U.S. Patent No. 174,465 for the speaking telegraph, or telephone, one of the most valuable patents in history. Attorneys for Bell and Elisha Gray had submitted their respective ideas for the telephone to the U.S. Patent Office three weeks earlier, incredibly, on the same day, “Valentine’s Day, February 14.” (The year 1876 should be assumed hereafter

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Memories occupy most of the silicon area in nowadays’ system-on-chips and contribute to a significant part of system power consumption. Though widely used, nonvolatile Flash memories still suffer from several drawbacks. Magnetic random access memories (MRAMs) have the potential to mitigate most of the Flash shortcomings. Moreover, it is predicted that they could be used for DRAM and SRAM replacement

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Electromagnetic waves, from radio signals to light, generally obey a strict symmetry in transmission: if they can travel from point A to point B, they can also travel backward from B to A with the same properties. This general symmetry, rooted in the fact that Maxwell’s equations are symmetric with respect to time and known as reciprocity, has profound implications on a variety of technologies and

This tutorial provides an intuitive and concrete description of the phenomena of electromagnetic nonreciprocity that will be useful for readers with engineering or physics backgrounds. The notion of time reversal and its different definitions are discussed with special emphasis on its relationship with the reciprocity concept. Starting from the Onsager reciprocal relations that are generally applicable