PirateBay Download | guo s machine learning commodity tiny devices theory practice 2023

guo s machine learning commodity tiny devices theory practice 2023

Type:: E-books
Files:: 1
Size:: 30.3 MB
Uploaded On:: Nov. 13, 2022, 8:28 a.m.
Added By:: andryold1
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Info Hash:: 5239F044D722253776CB4417DEF8B99526D45AB9

Textbook in PDF format This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. Fundamentals: On-Device Learning Paradigm Preliminary: Theories and Algorithms Model-Level Design: Computation Acceleration and Communication Saving Hardware-Level Design: Neural Engines and Tensor Accelerators Infrastructure-Level Design: Serverless and Decentralized Machine Learning System-Level Design: From Standalone to Clusters Application: Image-Based Visual Perception Application: Video-Based Real-Time Processing Application: Privacy, Security, Robustness and Trustworthiness in Edge AI

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Guo S. Machine Learning on Commodity Tiny Devices. Theory and Practice 2023.pdf 30.3 MB

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guo s machine learning commodity tiny devices theory practice 2023

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