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Cross-Layer Approximation and In-Network Acceleration: Enabling the Next Generation of Sustainable and High-Performance Reconfigurable Systems - Hardcover
Synopsis
This book presents a novel Cross-Layer Approximation and Distribution architecture and methodology that advances the design of high-performance, high-throughput, energy-efficient, and sustainable reconfigurable computing systems. By leveraging the error tolerance inherent in modern AI and signal processing workloads, it enables performance and energy gains across hardware and software layers. The authors introduce innovative approximate multipliers, dividers, and coarse-grained processing elements for FPGA and CGRA platforms, coupled with an error-resiliency analysis and a heuristic-driven optimization framework that dynamically balance performance and accuracy. Extending beyond conventional architectures, the methodology described also integrates novel In-Network Computing (INC) techniques to bring computation closer to data sources within 5G/6G infrastructures. The result is a cohesive, scalable approach that redefines how energy-efficient and adaptive computing can be achieved across the edge-to-cloud continuum.
- Describes a unified perspective on how approximation techniques can be applied across multiple abstraction levels;
- Discusses how FPGAs, CGRAs and In-Network Computing can enable scalable, distributed, and low-latency computation;
- Introduces architectural designs such as approximate multipliers, dividers, and hybrid SIMD/MIMD processing elements.
"synopsis" may belong to another edition of this title.
About the Author
Zahra Ebrahimi received her B.Sc. and M.Sc. degrees in Computer Engineering from Sharif University of Technology (SUT), Iran, where she also worked as a Research Assistant at the Data Storage, Networks, and Processing Laboratory. She earned her Ph.D. in Computer Science from the Center for Advancing Electronics Dresden (cfaed), Technische Universität Dresden, Germany, in 2025. During her Ph.D., she was awarded two entrepreneurial research fellowships from German ministries for the X-DNet (Software Campus Program, collaborated with Huawei) and GREEN-DNN (Mission KI startup acceleration) projects. Zahra is currently a Postdoctoral Research Associate at Ruhr-Universität Bochum. Her research focuses on cross-layer approximation, reconfigurable accelerator design, and energy-efficient, high-performance architectures for the edge-to-cloud continuum.
Akash Kumar (SM’13) received the joint Ph.D. degree in electrical engineering and embedded systems from the Eindhoven University of Technology, Eindhoven, The Netherlands, and the National University of Singapore (NUS), Singapore, in 2009. From 2009 to 2015, he was with NUS. From October 2015 until March 2024, he was a Professor with Technische Universität Dresden, Dresden, Germany, where he was directing the Chair for Processor Design. Since April 2024, he is directing the chair of Embedded Systems at Ruhr University Bochum, Germany. His research interests include the design and analysis of low-power embedded multiprocessor systems, and designing secure systems with emerging nano-technologies.
From the Back Cover
This book presents a novel Cross-Layer Approximation and Distribution architecture and methodology that advances the design of high-performance, high-throughput, energy-efficient, and sustainable reconfigurable computing systems. By leveraging the error tolerance inherent in modern AI and signal processing workloads, it enables performance and energy gains across hardware and software layers. The authors introduce innovative approximate multipliers, dividers, and coarse-grained processing elements for FPGA and CGRA platforms, coupled with an error-resiliency analysis and a heuristic-driven optimization framework that dynamically balance performance and accuracy. Extending beyond conventional architectures, the methodology described also integrates novel In-Network Computing (INC) techniques to bring computation closer to data sources within 5G/6G infrastructures. The result is a cohesive, scalable approach that redefines how energy-efficient and adaptive computing can be achieved across the edge-to-cloud continuum.
- Describes a unified perspective on how approximation techniques can be applied across multiple abstraction levels;
- Discusses how FPGAs, CGRAs and In-Network Computing can enable scalable, distributed, and low-latency computation;
- Introduces architectural designs such as approximate multipliers, dividers, and hybrid SIMD/MIMD processing elements.
"About this title" may belong to another edition of this title.
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Cross-Layer Approximation and In-Network Acceleration
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a novel Cross-Layer Approximation and Distribution architecture and methodology that advances the design of high-performance, high-throughput, energy-efficient, and sustainable reconfigurable computing systems. By leveraging the error tolerance inherent in modern AI and signal processing workloads, it enables performance and energy gains across hardware and software layers. The authors introduce innovative approximate multipliers, dividers, and coarse-grained processing elements for FPGA and CGRA platforms, coupled with an error-resiliency analysis and a heuristic-driven optimization framework that dynamically balance performance and accuracy. Extending beyond conventional architectures, the methodology described also integrates novel In-Network Computing (INC) techniques to bring computation closer to data sources within 5G/6G infrastructures. The result is a cohesive, scalable approach that redefines how energy-efficient and adaptive computing can be achieved across the edge-to-cloud continuum.Describes a unified perspective on how approximation techniques can be applied across multiple abstraction levels;Discusses how FPGAs, CGRAs and In-Network Computing can enable scalable, distributed, and low-latency computation;Introduces architectural designs such as approximate multipliers, dividers, and hybrid SIMD/MIMD processing elements. Seller Inventory # 9783032217110
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a novel Cross-Layer Approximation and Distribution architecture and methodology that advances the design of high-performance, high-throughput, energy-efficient, and sustainable reconfigurable computing systems. By leveraging the error tolerance inherent in modern AI and signal processing workloads, it enables performance and energy gains across hardware and software layers. The authors introduce innovative approximate multipliers, dividers, and coarse-grained processing elements for FPGA and CGRA platforms, coupled with an error-resiliency analysis and a heuristic-driven optimization framework that dynamically balance performance and accuracy. Extending beyond conventional architectures, the methodology described also integrates novel In-Network Computing (INC) techniques to bring computation closer to data sources within 5G/6G infrastructures. The result is a cohesive, scalable approach that redefines how energy-efficient and adaptive computing can be achieved across the edge-to-cloud continuum.Describes a unified perspective on how approximation techniques can be applied across multiple abstraction levels;Discusses how FPGAs, CGRAs and In-Network Computing can enable scalable, distributed, and low-latency computation;Introduces architectural designs such as approximate multipliers, dividers, and hybrid SIMD/MIMD processing elements.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 240 pp. Englisch. Seller Inventory # 9783032217110
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