Hardware Acceleration for Machine Learning
Language: English
Published by MDPI AG, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 121.29
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - This Reprint, titled 'Hardware Acceleration for Machine Learning', provides a coherent and updated snapshot of the rapidly evolving ecosystem for machine learning acceleration. As hardware platforms transition from general-purpose solutions to specialized heterogeneous architectures, this collection synthesizes cutting-edge developments essential for researchers and designers. The eight contributions within this Reprint cover a full spectrum of advancements, from low-power IoT applications and embedded FPGA and SoC implementations to ASIC edge solutions and efficient data-processing algorithms. Key research presented includes multi-dimensional benchmarking of deep learning inference frameworks, novel power-optimized RTL designs for CNN accelerators, unified FPGA/CGRA pipelines for time-critical edge AI, and new adaptive optimizers designed for training efficiency. Further studies address the performance impacts of data preprocessing techniques, general-purpose hyperdimensional computing accelerators, and innovative approaches to overcoming resource constraints in edge AI through DSP-free architectures and integer-state dynamics in quantized spiking neural networks. By combining these diverse strategies, this collection offers a comprehensive reference for those interested in building implementable, scalable, and energy-sustainable hardware platforms for next-generation intelligent systems.…
Seller Inventory # 9783725878550
- Title
- Hardware Acceleration for Machine Learning
- Publisher
- MDPI AG
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3725878552
- ISBN 13
- 9783725878550
- Item weight
- 705 grams
- Dimensions
- 250x175x18 mm
This Reprint, titled "Hardware Acceleration for Machine Learning", provides a coherent and updated snapshot of the rapidly evolving ecosystem for machine learning acceleration. As hardware platforms transition from general-purpose solutions to specialized heterogeneous architectures, this collection synthesizes cutting-edge developments essential for researchers and designers. The eight contributions within this Reprint cover a full spectrum of advancements, from low-power IoT applications and embedded FPGA and SoC implementations to ASIC edge solutions and efficient data-processing algorithms. Key research presented includes multi-dimensional benchmarking of deep learning inference frameworks, novel power-optimized RTL designs for CNN accelerators, unified FPGA/CGRA pipelines for time-critical edge AI, and new adaptive optimizers designed for training efficiency. Further studies address the performance impacts of data preprocessing techniques, general-purpose hyperdimensional computing accelerators, and innovative approaches to overcoming resource constraints in edge AI through DSP-free architectures and integer-state dynamics in quantized spiking neural networks. By combining these diverse strategies, this collection offers a comprehensive reference for those interested in building implementable, scalable, and energy-sustainable hardware platforms for next-generation intelligent systems.
"Synopsis" may belong to another edition of this title.
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