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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.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents the back cover for this issue of the publication.

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Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

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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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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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

Nonreciprocal components, such as isolators and circulators, are crucial components for photonic systems. In this article, we review theoretical and experimental progress toward developing nonreciprocal photonic devices based on dynamic modulation. In particular, we focus on approaches that operate at optical wavelengths and device architectures that have the potential for chip-scale integration. We

Charles Townes’s original laser proposal envisioned a low-power coherent light source for communications. Theodore Maiman’s demonstration of the first laser at Hughes Research Laboratories in 1960 produced bright pulses of red light and gave great hopes for bigger and better things, especially in the Pentagon. Two years later, a Sunday newspaper supplement feature titled “The Incredible Laser: Death

This Special Issue on Multienergy Systems is motivated by the tremendous changes and opportunities in integrated planning, operation, and modeling of the energy systems, including electric, natural gas, and district heating–cooling systems together with the demand side. Traditionally, the role of electric power has been regarded as an energy carrier between the power plants and the consumers. Consequently

This article takes an integrated view of optimized capacity design and operation of islanded energy hubs. We consider energy hubs that incorporate emerging distributed energy resources as well as energy storage devices and fully support electricity and heat demand of an islanded installation. Both battery and hydrogen storage are incorporated. To explicitly account for the stochasticity in renewable

In many parts of the world, electric power systems have seen a significant shift toward generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty

Natural-gas and power systems are increasingly interdependent due to the integration of an increasing number of combined cycle gas turbines in the power generation mix. However, natural gas and power systems are generally independently operated. This is the result of history and the fact that natural gas has not been important for electricity production until recently. Adopting a power system perspective

We derive conditions for monotonicity properties that characterize general flows of a commodity over a network, where the flow is described by potential and flow dynamics on the edges, as well as potential continuity and the Kirchhoff–Neumann mass balance requirements at nodes. The transported commodity may be injected or withdrawn at any of the network nodes, and its movement throughout the network

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with

Water pumping in drinking water distribution networks (WDNs) can be treated as a flexible load in the power distribution network (PDN). In this article, we formulate an optimization problem to minimize the electricity costs associated with pumping subject to WDN and PDN constraints. In practice, both water and power demands are uncertain and pumps should be scheduled to ensure that pump operation does

Approximate computing is an emerging paradigm that, by relaxing the requirement for full accuracy, offers benefits in terms of design area and power consumption. This paradigm is particularly attractive in applications where the underlying computation has inherent resilience to small errors. Such applications are abundant in many domains, including machine learning, computer vision, and signal processing

Zeroth-order optimization algorithms are an attractive alternative for stochastic optimization problems, when gradient computations are expensive or when closed-form loss functions are not available. Recently, there has been a surge of activity in utilizing zeroth-order optimization algorithms in myriads of applications including black-box adversarial attacks on machine learning frameworks, reinforcement

Approximate computing has emerged as a new paradigm for high-performance and energy-efficient design of circuits and systems. For the many approximate arithmetic circuits proposed, it has become critical to understand a design or approximation technique for a specific application to improve performance and energy efficiency with a minimal loss in accuracy. This article aims to provide a comprehensive

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given “difficult” (constrained) problem via finding solutions of a sequence of “easier” (often unconstrained) subproblems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables. ALM is highly flexible with respect to how primal subproblems can

Numerical optimization serves as one of the pillars of machine learning. To meet the demands of big data applications, lots of efforts have been put on designing theoretically and practically fast algorithms. This article provides a comprehensive survey on accelerated first-order algorithms with a focus on stochastic algorithms. Specifically, this article starts with reviewing the basic accelerated

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Presents the table of contents for this issue of the publication.

The articles in this month’s special issue provide insight into future computing technologies such as novel architectures, spintronic memories, and quantum computing.

Computing systems are evolving rapidly. At the device level, emerging devices are beginning to compete with traditional CMOS systems. At the architecture level, novel architectures are successfully avoiding the communication bottleneck that is a central feature, and a central limitation, of the von Neumann architecture. Furthermore, such systems are increasingly plagued by unreliability. This unreliability

Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers

On January 26, 1926 (see Fig. 1 ), John Logie Baird gave a demonstration at his laboratory in 22 Frith Street, London, of the live transmission of moving images, obtained in reflected light with tonal graduation, to members of the Royal Institution. This event is generally accepted as the first public demonstration of true television [1] , [2, pp. 88–107] , [3, pp. 65–84] . Ten months earlier, on March

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