5G Enabled Energy Innovation: Advanced Wireless Networks for Science (Workshop Report)

Beckman, Pete; Catlett, Charlie; Ahmed, Moinuddin; Alawad, Mohammed; Bai, Linquan; Balaprakash, Prasanna; Barker, Kevin; Berry, Randall A.; Bhuyan, Arup; Brebner, Gordon; Burkes, Klaehn; Butko, Anastasiia; Cappello, Franck; Chard, Ryan; Collis, Scott; Cree, Johnathan; Dasgupta, Dipankar; Evdokimov, A.; Fields, Jason; Fuhr, Peter L.; Harper, Colby; Jin, Yier; Kettimuthu, Rajkumar; Kiran, Mariam; Kozma, Róbert; Kumar, Praveen; Kumar, Y Akshay; Luo, Linqing; Mashayekhy, Lena; Monga, Inder; Nickless, Bill; Pappas, Thrasyvoulos N.; Peterson, Elena S.; Pfeffer, Trever; Rakheja, Shaloo; Tribaldos, Veroica; Rooke, Sterling; Roy, Sumit; Saadawi, Tarek; Sandy, Alec; Sankaran, Rajesh; Schwarz, Nina; Somnath, Suhas; Stan, Marius; Stuart, Cory; Ryan, Sullivan,; Sumant, Anirudha V.; Tchilinguirian, G.; Tran, N. V.; Veeramany, Arun; Wang, Angela Yee-Moon; Wang, Bin; Wiedlea, Andrew; Wielandt, Stijn; Windus, Theresa L.; Wu, Yuxin; Yang, Xi; Yao, Zhi; Yu, Rose; Zeng, Yuping; Zhang, Yuepeng

doi:10.2172/1606538

Cited by 4 publications

(5 citation statements)

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“…Cloud service providers are developing edge computing platforms that extend their cloud infrastructure to the network's edge, enabling organizations to deploy and manage edge applications more seamlessly. These platforms provide tools and services for developing, deploying, and managing edge applications, including edge orchestration, data synchronization, and security management (Beckman, et al, 2020;Adegoke et al, 2023).…”

Section: Technological Developments In Edge Computingmentioning

confidence: 99%

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

Enoch Oluwademilade Sodiya,

Uchenna Joseph Umoga,

Alexander Obaigbena

et al. 2024

Int. J. Sci. Res. Arch.

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The burgeoning growth of the Internet of Things (IoT) has prompted a paradigm shift in computing architectures, leading to the emergence and rapid evolution of edge computing. This review delves into the current state and prospects of edge computing within the IoT ecosystem, exploring its significance, challenges, and future potential. Edge computing, characterized by decentralized data processing at or near the source of data generation, has gained substantial traction owing to its ability to address critical concerns such as latency, bandwidth consumption, and privacy issues inherent in centralized cloud-based systems. By enabling data processing closer to the point of collection, edge computing minimizes latency and ensures real-time responses, making it indispensable for latency-sensitive applications like industrial automation, autonomous vehicles, and healthcare. The integration of edge computing with IoT devices has facilitated the creation of distributed computing architectures capable of handling massive data volumes generated by interconnected devices. This convergence enables efficient data aggregation, analysis, and decision-making at the network's edge, reducing the burden on centralized cloud infrastructure and optimizing resource utilization. Despite its numerous advantages, edge computing faces several challenges, including resource constraints, security vulnerabilities, and interoperability issues. Resource-constrained edge devices often lack the computational power and storage capacity required for complex analytics, necessitating innovative approaches to resource management and workload distribution. Moreover, the distributed nature of edge environments introduces new security risks, necessitating robust security measures to safeguard sensitive data and mitigate cyber threats. Looking ahead, the prospects of edge computing within the IoT ecosystem are promising, with advancements in edge hardware, software, and networking technologies driving innovation and adoption. Edge computing is poised to play a pivotal role in enabling the next wave of IoT applications, ranging from smart cities and autonomous systems to immersive experiences and personalized services. However, realizing the full potential of edge computing requires concerted efforts from industry stakeholders to address existing challenges and foster ecosystem-wide collaboration. The convergence of edge computing and IoT holds immense potential to revolutionize industries, reshape computing architectures, and empower a new era of intelligent, responsive, and decentralized systems.

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Section: Technological Developments In Edge Computingmentioning

confidence: 99%

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

Enoch Oluwademilade Sodiya,

Uchenna Joseph Umoga,

Alexander Obaigbena

et al. 2024

Int. J. Sci. Res. Arch.

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“…As described in Figure 10-1, this includes the new development or extension of current metadata and ontology standards, new methods for optimizing the acquisition of data, automated data characterization and quality assessment, and new tools to enable cross-domain data discovery and distribution. At the same time, emerging automated and distributed data streams (e.g., 5G, Internet of Things [IoT], e.g., Beckman et al 2020;Kollias et al 2021;, novel collection platforms (e.g., unoccupied aerial systems [UASs]; Yang et al 2021), together with edge computing capability and AI/ML-guided observations (Balaprakash et al 2021) will require modern data and file standards that can be flexible enough to capture key spatial information (e.g., projection, resolution), allow for real-time updating of information (e.g., streaming data storage and distribution), and account for any data reduction (e.g., extracting a specific signal from a larger data stream) while maintaining provenance to original data sources. File formats also need to allow for capturing and storing QA/QC and uncertainty information from collection to distribution, including as many sources of uncertainty as possible.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, there are opportunities to provide new data services for AI/ML use cases. These can include, for example, edge computing to enable data analysis at the source, adaptive data collection, and new ontology capabilities to improve cross-domain data discovery (e.g., Pouchard et al 2013;Beckman et al 2020;Balaprakash et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

Hickmon¹,

Varadharajan²,

Hoffman³

et al. 2022

Self Cite

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“…Next-generation edge systems will operate under conditions where exporting all the acquired data for centralized processing is inconvenient or impossible [1]. Monitoring infrastructure for highly dynamic systems (e.g., sensor networks) will need to operate in low power settings with limited bandwidth available for communication [2]. Autonomous vehicles will need to make critical decisions in real-time in a distributed setting.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Synthesis of Dynamically Controlled Machine Learning Accelerators

Curzel

Agostini

Castellana

et al. 2022

IEEE Trans. Comput.

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Edge systems are required to autonomously make real-time decisions based on large quantities of input data under strict power, performance, area, and other constraints. Meeting these constraints is only possible by specializing systems through hardware accelerators purposefully built for machine learning and data analysis algorithms. However, data science evolves at a quick pace, and manual design of custom accelerators has high non-recurrent engineering costs: general solutions are needed to automatically and rapidly transition from the formulation of a new algorithm to the deployment of a dedicated hardware implementation. Our solution is the SOftware Defined Architectures (SODA) Synthesizer, an end-to-end, multi-level, modular, extensible compiler toolchain providing a direct path from machine learning tools to hardware. The SODA Synthesizer frontend is based on the multilevel intermediate representation (MLIR) framework; it ingests pre-trained machine learning models, identifies kernels suited for acceleration, performs high-level optimizations, and prepares them for hardware synthesis. In the backend, SODA leverages state-of-the-art high-level synthesis techniques to generate highly efficient accelerators, targeting both field programmable devices (FPGAs) and applicationspecific circuits (ASICs). In this paper, we describe how the SODA Synthesizer can also assemble the generated accelerators (based on the finite state machine with datapath model) in a custom system driven by a distributed controller, building a coarse-grained dataflow architecture that does not require a host processor to orchestrate parallel execution of multiple accelerators. We show the effectiveness of our approach by automatically generating ASIC accelerators for layers of popular deep neural networks (DNNs). Our high-level optimizations result in up to 74x speedup on isolated accelerators for individual DNN layers, and our dynamically scheduled architecture yields an additional 3x performance improvement when combining accelerators to handle streaming inputs.

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5G Enabled Energy Innovation: Advanced Wireless Networks for Science (Workshop Report)

Cited by 4 publications

References 0 publications

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

End-to-End Synthesis of Dynamically Controlled Machine Learning Accelerators

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