Hardware Acceleration for Machine Learning

ISBN 10: 3725878552 / ISBN 13: 9783725878550

Published by MDPI AG, 2026

About this Item

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.

Seller Inventory # 135854841

Bibliographic Details

• Title: Hardware Acceleration for Machine Learning
• Publisher: MDPI AG
• Publication Date: 2026
• Language: English
• ISBN 10: 3725878552
• ISBN 13: 9783725878550
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 18D millimeters 250 millimeters width by 175 millimeters height by 18 millimeters depth
• Weight: 705 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.