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Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
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| Pengarang | : | Md. Maruf Hossain Shuvo |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 111 (No. 1) |
| Halaman | : | 42-91 |
| Abstrak | : | Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications remains challenging. DL algorithms are often computationally expensive, power-hungry, and require large memory to process complex and iterative operations of millions of parameters. Hence, training and inference of DL models are typically performed on high-performance computing (HPC) clusters in the cloud. Data transmission to the cloud results in high latency, round-trip delay, security and privacy concerns, and the inability of real-time decisions. Thus, processing on edge devices can significantly reduce cloud transmission cost. Edge devices are end devices closest to the user, such as mobile phones, cyber–physical systems (CPSs), wearables, the Internet of Things (IoT), embedded and autonomous systems, and intelligent sensors. These devices have limited memory, computing resources, and power-handling capability. Therefore, optimization techniques at both the hardware and software levels have been developed to handle the DL deployment efficiently on the edge. Understanding the existing research, challenges, and opportunities is fundamental to leveraging the next generation of edge devices with artificial intelligence (AI) capability. Mainly, four research directions have been pursued for efficient DL inference on edge devices: 1) novel DL architecture and algorithm design; 2) optimization of existing DL methods; 3) development of algorithm–hardware codesign; and 4) efficient accelerator design for DL deployment. This article focuses on surveying each of the four research directions, providing a comprehensive review of the state-of-the-art tools and techniques for efficient edge inference. |
Machine Learning for Emergency Management: A Survey and Future Outlook
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 111 (No. 1) |
| Halaman | : | 19-41 |
| Abstrak | : | Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers in such situations and ultimately improve disaster prevention, preparedness, response, and recovery. In this survey article, we highlight relevant work in this area by first focusing on the commonalities of emergency management applications and key challenges that ML algorithms need to address. Then, we present a categorization of relevant works across all the emergency management phases and operations, highlighting the main algorithms used. Based on our review, we conclude that ML algorithms can provide the basis for tackling different activities across the emergency management phases with a unified algorithmic framework that can solve a large set of problems. Finally, through the systematic literature review, we provide promising future directions for utilizing ML algorithms more effectively in emergency management applications. More importantly, we identify the need for better generalization of algorithms, improved explainability, and trustworthiness of ML algorithms with respect to the emergency management personnel, as well as more efficient ways of addressing the challenges associated with building appropriate datasets. |
A perspective vision of micro/nano systems and technologies as enablers of 6g, superiot, and tactile internet
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 111 (No. 1) |
| Halaman | : | 5-18 |
| Abstrak | : | Modern research in technology fields, such as electronics, distributed networks of sensing/functional nodes, and wireless and wearable devices, is relentlessly converging around wide application paradigms, such as Internet of Things (IoT) [1] and Internet of Everything (IoE) [2]—Table 1, at the end of section, offers a full list of used acronyms. From a different perspective, recent advances in electronics, hardware (HW) technologies, information technology (IT), and artificial intelligence (AI) for telecommunication networks, standards, and protocols look to unavoidably fall under the umbrella of fifth generation of mobile communications (5G) [3]. Even though they appear orthogonal to each other, IoT, IoE, and 5G are closely linked together. In a nutshell, IoT and IoE target pervasivity of services, while 5G is the pillar upon which transmission of massive amounts of data and information should lay [4]. As brief recap, 5G poses on the three cornerstone drivers of enhanced mobile broadband (eMBB), massive machine-type communications (mMTCs), and ultrareliable low latency communications (URLLC) [5], to enable data-centric applications such as machine-to-machine (M2M), vehicle-to-vehicle (V2V), and vehicle-to-everything (V2X) communications, along with virtual reality (VR), augmented reality (AR), and extended reality (XR). |
High-Density Power Conversion and Wide-Bandgap Semiconductor Power Electronics Switching Devices
| Pengarang | : | Seppecher, Laurent,Niclas, Angele |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 12) |
| Halaman | : | 2308-2326 |
| Abstrak | : | Power electronics switching devices made on wide-bandgap (WBG) semiconductors are known to have the potential to make a transformative impact on 21st century energy economy. However, their market penetration has been slow primarily due to high cost and unknown application-level reliability. This article presents a comprehensive report on the history, current state of the art, and impending challenges in WBG power semiconductor technologies in order to break open this gridlock. |
Physical Layer Covert Communication in B5G Wireless Networks—its Research, Applications, and Challenges
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 1) |
| Halaman | : | 47 - 82 |
| Abstrak | : | Physical layer covert communication is a crucial secure communication technology that enables a transmitter to convey information covertly to a recipient without being detected by adversaries. Unlike typical cryptography and physical layer security systems that concentrate on protecting the sent signal content, covert communications seek to conceal the existence of legitimate transmission. Thus, with beyond fifth-generation (B5G) wireless communications, covert communications can operate in tandem or as a supplement to conventional security techniques. We provide an extensive overview of the basic theories and several strategies in physical layer covert communications in this article. In particular, we go into great detail about the basic theories of physical layer covert communications, such as channel models, codes, secret keys, and covertness metrics, as well as various covert schemes in progressively more complicated scenarios, such as covert communications in single-antenna and multiantenna three-node systems and covert communications in jammer- and relay-aided systems. In addition, we identify the challenges and future directions for research on covert communications in B5G wireless networks. |
When Robotics Meets Wireless Communications: An Introductory Tutorial
| Pengarang | : | John R. Conrad |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 2) |
| Halaman | : | 140-177 |
| Abstrak | : | The importance of ground mobile robots (MRs) and unmanned aerial vehicles (UAVs) within the research community, industry, and society is growing fast. Nowadays, many of these agents are equipped with communication systems that are, in some cases, essential to successfully achieve certain tasks. In this context, we have begun to witness the development of a new interdisciplinary research field at the intersection of robotics and communications. This research field has been boosted by the intention of integrating UAVs within the 5G and 6G communication networks and will undoubtedly lead to many important applications in the near future. Nevertheless, one of the main obstacles to the development of this research area is that most researchers address these problems by oversimplifying either the robotics or the communications aspects. Doing so impedes the ability to reach the full potential of this new interdisciplinary research area. In this tutorial, we present some of the modeling tools necessary to address problems involving both robotics and communication from an interdisciplinary perspective. As an illustrative example of such problems, we focus on the issue of communication-aware trajectory planning in this tutorial. |
Cloud-Native Computing: A Survey From the Perspective of Services
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 1) |
| Halaman | : | 12-46 |
| Abstrak | : | The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clear research roadmap on this topic is still missing. As a contribution to this knowledge, this article surveys key issues during the life cycle of cloud-native applications, from the perspective of services. Specifically, we elaborate on the research domains by decoupling the life cycle of cloud-native applications into four states: building, orchestration, operation, and maintenance. We also discuss the fundamental necessities and summarize the key performance metrics that play critical roles during the development and management of cloud-native applications. We highlight the key implications and limitations of existing works in each state. The challenges, future directions, and research opportunities are also discussed. |
Trustworthy Graph Neural Networks: Aspects, Methods, and Trends
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 2) |
| Halaman | : | 97-139 |
| Abstrak | : | Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications such as recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only requirement for GNNs. Performance-oriented GNNs have exhibited potential adverse effects, such as vulnerability to adversarial attacks, unexplainable discrimination against disadvantaged groups, or excessive resource consumption in edge computing environments. To avoid these unintentional harms, it is necessary to build competent GNNs characterized by trustworthiness. To this end, we propose a comprehensive roadmap to build trustworthy GNNs from the view of the various computing technologies involved. In this survey, we introduce basic concepts and comprehensively summarize existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. In addition, we highlight the intricate cross-aspect relations between the above six aspects of trustworthy GNNs. Finally, we present a thorough overview of trending directions for facilitating the research and industrialization of trustworthy GNNs. |
Informing Machine Perception With Psychophysics
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 2) |
| Halaman | : | 88-96 |
| Abstrak | : | Gustav Fechner’s 1860 delineation of psychophysics, the measurement of sensation in relation to its stimulus, is widely considered to be the advent of modern psychological science. In psychophysics, a researcher parametrically varies some aspects of a stimulus and measures the resulting changes in a human subject’s experience of that stimulus; doing so gives insight into the determining relationship between a sensation and the physical input that evoked it. This approach is used heavily in perceptual domains, including signal detection, threshold measurement, and ideal observer analysis. Scientific fields, such as vision science, have always leaned heavily on the methods and procedures of psychophysics, but there is now growing appreciation of them by machine learning researchers, sparked by widening overlap between biological and artificial perception [1], [2], [3], [4], [5]. Machine perception that is guided by behavioral measurements, as opposed to guidance restricted to arbitrarily assigned human labels, has significant potential to fuel further progress in artificial intelligence (AI). |
Drawing the Boundaries Between Blockchain and Blockchain-Like Systems: A Comprehensive Survey on Distributed Ledger Technologies
| Pengarang | : | Emir, Threes |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 112 (No. 3) |
| Halaman | : | 247-299 |
| Abstrak | : | Bitcoin’s success as a global cryptocurrency has paved the way for the emergence of blockchain, a revolutionary category of distributed systems. However, the growing popularity of blockchain has led to a significant divergence from its core principles in many systems labeled as “blockchain.” This divergence has introduced complexity into the blockchain ecosystem, exacerbated by a lack of comprehensive reviews on blockchain and its variants. Consequently, gaining a clear and updated understanding of the diverse spectrum of current blockchain and blockchain-like systems has become challenging. This situation underscores the necessity for an extensive literature review and the development of thematic taxonomies. This survey seeks to offer a comprehensive and current assessment of existing blockchains and their variations while delineating the boundaries between blockchain and blockchain-like systems. To achieve this objective, we propose a holistic reference model for conceptualizing and analyzing these systems. Our layer-wise framework envisions all distributed ledger technologies (DLTs) as composed of four principal layers: data, consensus, execution, and application (DCEA). In addition, we introduce a new taxonomy that enhances the classification of blockchain and blockchain-like systems, offering a more useful perspective than existing works. Furthermore, we conduct a state-of-the-art review from a layered perspective, employing 23 evaluative criteria predefined by our framework. We perform a qualitative and quantitative comparative analysis of 44 DLT solutions and 26 consensus mechanisms while discussing differences and boundaries between blockchain and blockchain-like systems. We emphasize the significant challenges and tradeoffs encountered by distributed ledger designers, decision-makers, and project managers during the design or adoption of a DLT solution. Finally, we outline crucial research challenges and directions in the field of DLTs. |