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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.
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Hardware Acceleration for Machine Learning
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Hardcover. Zustand: new. Hardcover. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783725878550
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Hardware Acceleration for Machine Learning (Hardcover)
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Hardcover. Zustand: new. Hardcover. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9783725878550
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Hardcover. Zustand: new. Hardcover. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9783725878550
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Buch. Zustand: Neu. Hardware Acceleration for Machine Learning | Buch | Englisch | 2026 | MDPI AG | EAN 9783725878550 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 135854841
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