Tiny Machine Learning Techniques For Constrained Devices

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Tiny Machine Learning Techniques For Constrained Devices

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Author : Khalid El-Makkaoui
language : en
Publisher: CRC Press
Release Date : 2026-01-30

Tiny Machine Learning Techniques For Constrained Devices written by Khalid El-Makkaoui and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-30 with Computers categories.

Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments. This book offers thorough coverage of key topics, including: Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints. Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies. Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices. Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation. Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students. This book is an essential resource for embedded system designers, AI practitioners, cybersecurity professionals, and academics who want to harness the power of TinyML for smarter, more efficient, and secure edge intelligence solutions.

Tiny Machine Learning Design Principles And Applications

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Author : Agbotiname Lucky Imoize
language : en
Publisher: John Wiley & Sons
Release Date : 2026-01-05

Tiny Machine Learning Design Principles And Applications written by Agbotiname Lucky Imoize and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-05 with Computers categories.

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Computational Science And Its Applications Iccsa 2024 Workshops

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Author : Osvaldo Gervasi
language : en
Publisher: Springer Nature
Release Date : 2024-07-30

Computational Science And Its Applications Iccsa 2024 Workshops written by Osvaldo Gervasi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with Computers categories.

This eleven-volume set LNCS 14815 – 14825 constitutes the refereed workshop proceedings of the 24th International Conference on Computational Science and Its Applications, ICCSA 2024, held at Hanoi, Vietnam, during July 1–4, 2024. The 281 full papers, 17 short papers and 2 PHD showcase papers included in this volume were carefully reviewed and selected from a total of 450 submissions. In addition, the conference consisted of 55 workshops, focusing on very topical issues of importance to science, technology and society: from new mathematical approaches for solving complex computational systems, to information and knowledge in the Internet of Things, new statistical and optimization methods, several Artificial Intelligence approaches, sustainability issues, smart cities and related technologies.

Data Science And Big Data Analytics

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Author : Durgesh Mishra
language : en
Publisher: Springer Nature
Release Date : 2026-01-10

Data Science And Big Data Analytics written by Durgesh Mishra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-10 with Computers categories.

This book features high-quality research papers presented at the Fifth International Conference on Data Science and Big Data Analytics (IDBA 2025), organized by Symbiosis University of Applied Sciences, Indore, India, in association with ACM and IEEE Computer Society in hybrid mode during June 27–28, 2025. This book discusses topics such as data science, artificial intelligence, machine learning, quantum computing, big data and cloud security, computation security, big data security, information security, forecasting, data analytics, mathematics for data science, graph theory and application in data science, data visualization, computer vision, and analytics for social networks.

Proceedings Of The First International Conference On Data Engineering And Machine Intelligence

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Author : S. Rakesh Kumar
language : en
Publisher: Springer Nature
Release Date : 2024-12-20

Proceedings Of The First International Conference On Data Engineering And Machine Intelligence written by S. Rakesh Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Computers categories.

This volume constitutes peer-reviewed proceedings of the First International Conference on Data Engineering and Machine Intelligence, ICDEMI 2023. The research problems about data engineering and machine learning are covered in this book. The proceedings cover recent contributions and novel developments from researchers across industry and academia in data science, data engineering, artificial intelligence, big data, cloud computing, sustainability, and knowledge-based expert systems from technological and social perspectives. This book will serve as a valuable reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners.

Intelligent Systems And Advanced Computing Sciences

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Author : Hani Hagras
language : en
Publisher: Springer Nature
Release Date : 2025-07-01

Intelligent Systems And Advanced Computing Sciences written by Hani Hagras and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Computers categories.

This book constitutes revised selected papers from the thoroughly refereed conference proceedings of the 4th International Conference on Intelligent Systems and Advanced Computing Sciences, ISACS 2023, which took place in Taza, Morocco, in October 26–27, 2023. The 30 full papers and 8 short papers presented in these proceedings were carefully reviewed and selected from 131 submissions. This conference focusing on all theoretical and practical aspects related to information technology and communications security.

Technology 2000 Volume 1

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Author :
language : en
Publisher:
Release Date : 1991

Technology 2000 Volume 1 written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.

Efficient Algorithms And Systems For Tiny Deep Learning

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Author : Ji Lin (Researcher in computer science)
language : en
Publisher:
Release Date : 2021

Efficient Algorithms And Systems For Tiny Deep Learning written by Ji Lin (Researcher in computer science) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Tiny machine learning on IoT devices based on microcontroller units (MCUs) enables various real-world applications (e.g., keyword spotting, anomaly detection). However, deploying deep learning models to MCUs is challenging due to the limited memory size: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. In this thesis, we study efficient algorithms and systems for tiny-scale deep learning. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. device, latency, energy, memory) under low search costs. TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the search space and fit a larger model. TinyEngine adapts the memory scheduling according to the overall network topology rather than layer-wise optimization, reducing the memory usage by 3.4x, and accelerating the inference by 1.7-3.3x compared to TF-Lite Micro and CMSIS-NN. For vision applications on MCUs, we diagnosed and found that existing convolutional neural network (CNN) designs have an imbalanced peak memory distribution: the first several layers have much higher peak memory usage than the rest of the network. Based on the observation, we further extend the framework to support patch-based inference to break the memory bottleneck of the initial stage. MCUNet is the first to achieves>70% ImageNet top1 accuracy on an off-the-shelf commercial microcontroller, using 3.5x less SRAM and 5.7x less Flash compared to quantized MobileNetV2 and ResNet-18. On visual & audio wake words tasks, MCUNet achieves state-of-the-art accuracy and runs 2.4- 3.4x faster than MobileNetV2 and ProxylessNAS-based solutions with 3.7-4.1x smaller peak SRAM. Our study suggests that the era of always-on tiny machine learning on IoT devices has arrived.

Tinyml On Microcontrollers

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Author : Kalen Virell
language : en
Publisher: Independently Published
Release Date : 2025-08-16

Tinyml On Microcontrollers written by Kalen Virell and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-16 with Technology & Engineering categories.

TinyML is bringing machine learning to the smallest devices-microcontrollers. Imagine running neural networks on a battery-powered sensor, a wearable device, or a smart appliance, all without needing a cloud connection. This book shows you how to implement efficient models that fit in the tightest of spaces, even with limited processing power and memory. TinyML on Microcontrollers is a hands-on guide that walks you through designing, deploying, and optimizing machine learning models on resource-constrained hardware. You'll learn how to build custom models for real-time inference, develop efficient data pipelines, and fine-tune models for performance and memory usage. From training on full datasets to converting models for deployment using quantization and pruning, this book covers all the essential techniques. You'll work through step-by-step projects using popular microcontroller platforms like Arduino, Raspberry Pi, and TensorFlow Lite for Microcontrollers. The book also covers tools like Edge Impulse for model development and deployment, providing you with the skills to move from prototypes to fully functional devices. Topics like sensor integration, low-power consumption, and optimizing code to run on minimal resources make this a practical, real-world guide. By the end of this book, you'll be able to take machine learning from the cloud to the edge, running powerful neural networks on small, cost-effective microcontrollers. Buy this book now and start building TinyML applications that bring intelligent systems to life, even in the most resource-constrained environments.

Machine Learning On Commodity Tiny Devices

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Author : Song Guo
language : en
Publisher: CRC Press
Release Date : 2022-12-13

Machine Learning On Commodity Tiny Devices written by Song Guo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-13 with Computers categories.

This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book 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. This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems.