Embedded Deep Learning
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Embedded Deep Learning
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Author : Bert Moons
language : en
Publisher: Springer
Release Date : 2018-10-23
Embedded Deep Learning written by Bert Moons and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-23 with Technology & Engineering categories.
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
Deep Learning On Embedded Systems
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Author : Tariq M. Arif
language : en
Publisher: John Wiley & Sons
Release Date : 2025-04-29
Deep Learning On Embedded Systems written by Tariq M. Arif 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 2025-04-29 with Technology & Engineering categories.
Comprehensive, accessible introduction to deep learning for engineering tasks through Python programming, low-cost hardware, and freely available software Deep Learning On Embedded Systems is a comprehensive guide to the practical implementation of deep learning for engineering tasks through computers and embedded hardware such as Raspberry Pi and Nvidia Jetson Nano. After an introduction to the field, the book provides fundamental knowledge on deep learning, convolutional and recurrent neural networks, computer vision, and basics of Linux terminal and docker engines. This book shows detailed setup steps of Jetson Nano and Raspberry Pi for utilizing essential frameworks such as PyTorch and OpenCV. GPU configuration and dependency installation procedure for using PyTorch is also discussed allowing newcomers to seamlessly navigate the learning curve. A key challenge of utilizing deep learning on embedded systems is managing limited GPU and memory resources. This book outlines a strategy of training complex models on a desktop computer and transferring them to embedded systems for inference. Also, students and researchers often face difficulties with the varying probabilistic theories and notations found in data science literature. To simplify this, the book mainly focuses on the practical implementation part of deep learning using Python programming, low-cost hardware, and freely available software such as Anaconda and Visual Studio Code.To aid in reader learning, questions and answers are included at the end of most chapters. Written by a highly qualified author, Deep Learning On Embedded Systems includes discussion on: Fundamentals of deep learning, including neurons and layers, activation functions, network architectures, hyperparameter tuning, and convolutional and recurrent neural networks (CNNs & RNNs) PyTorch, OpenCV, and other essential framework setups for deep transfer learning, along with Linux terminal operations, docker engine, docker images, and virtual environments in embedded devices. Training models for image classification and object detection with classification, then converting trained PyTorch models to ONNX format for efficient deployment on Jetson Nano and Raspberry Pi. Deep Learning On Embedded Systems serves as an excellent introduction to the field for undergraduate engineering students seeking to learn deep learning implementations for their senior capstone or class projects and graduate researchers and educators who wish to implement deep learning in their research.
Embedded Machine Learning For Cyber Physical Iot And Edge Computing
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Author : Sudeep Pasricha
language : en
Publisher: Springer Nature
Release Date : 2023-10-09
Embedded Machine Learning For Cyber Physical Iot And Edge Computing written by Sudeep Pasricha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-09 with Computers categories.
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
Embedded Artificial Intelligence Bridging The Gap Between Hardware And Deep Learning
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Author : François Rivet
language : en
Publisher: CRC Press
Release Date : 2026-01-26
Embedded Artificial Intelligence Bridging The Gap Between Hardware And Deep Learning written by François Rivet and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-26 with Computers categories.
The IEEE CAS Seasonal School on Technologies for Artificial Intelligence tackles the critical skill gap between embedded technology and deep learning. Supported by European projects FVLLMONTI, HERMES, and RadioSpin, this event fosters a transdisciplinary community focused on embedded artificial intelligence. Modern AI and deep learning often require extensive computing resources, impacting security and privacy. Embedded AI offers a solution by running machine learning models on edge devices, necessitating optimized software–hardware integration and energy-efficient neural network hardware. This school equips participants with the skills to innovate in circuit design and execute data-intensive applications on limited-resource devices. The curriculum covers neural network basics, hardware enhancement, electrical characterization, and neuromorphic device design. Key topics include 6G transceiver optimization, transformer architectures for machine translation, and intelligent sensors for practical applications like RF fingerprint recognition and breast cancer detection.
Tinyml
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Author : Pete Warden
language : en
Publisher: O'Reilly Media
Release Date : 2019-12-16
Tinyml written by Pete Warden and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-16 with Computers categories.
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Deep Learning On Edge Computing Devices
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Author : Xichuan Zhou
language : en
Publisher: Elsevier
Release Date : 2022-02-02
Deep Learning On Edge Computing Devices written by Xichuan Zhou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Computers categories.
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. - Focuses on hardware architecture and embedded deep learning, including neural networks - Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications - Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud - Describes how to maximize the performance of deep learning on Edge-computing devices - Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring
Embedded Deep Learning Generative Ai Algorithms
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Author : Muhammad Asim
language : en
Publisher: Independently Published
Release Date : 2025-03-08
Embedded Deep Learning Generative Ai Algorithms written by Muhammad Asim and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-08 with Computers categories.
Embedded deep learning & generative AI algorithms offer a systematic exploration of deep learning and generative artificial intelligence within embedded systems, meticulously crafted to equip readers with theoretical foundations and applied expertise. Structured as a progressive intellectual journey, the text methodically shepherds readers from elementary principles to sophisticated implementations while addressing the nuanced complexities inherent in resource-constrained environments. Bringing abstract algorithmic frameworks with pragmatic engineering considerations fosters a pedagogical synergy between innovation and practicality, underscored by case studies and industry-relevant scenarios illuminating the intersection of cutting-edge AI and embedded architectures. The treatise prioritizes not only conceptual mastery but also the cultivation of problem-solving acumen, preparing practitioners to navigate the evolving landscape of intelligent systems design amidst real-world constraints.
Embedded Machine Learning With Microcontrollers
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Author : Cem Ünsalan
language : en
Publisher: Springer Nature
Release Date : 2024-10-24
Embedded Machine Learning With Microcontrollers written by Cem Ünsalan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-24 with Computers categories.
This textbook introduces basic embedded machine learning methods by exploring practical applications on STM32 development boards. Covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers, microprocessor systems, and embedded systems. Following the learning by doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples that will provide them with the design and implementation skills needed for a competitive job market. By utilizing a programming environment that enables students to reach and modify low-level microcontroller properties, the material allows for more control of the developed system. Students will be guided in implementing machine learning methods to be deployed and tested on microcontrollers throughout the book, with the theory behind the implemented methods also emphasized. Sample codes and course slides are available for readers and instructors, and a solutions manual is available to instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists.
An Embedded Deep Learning System For Identifying And Counting Objects Traffic
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Author :
language : en
Publisher:
Release Date : 2022
An Embedded Deep Learning System For Identifying And Counting Objects Traffic written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Embedded Machine Learning For Cyber Physical Iot And Edge Computing
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Author : Sudeep Pasricha
language : en
Publisher: Springer Nature
Release Date : 2023-10-06
Embedded Machine Learning For Cyber Physical Iot And Edge Computing written by Sudeep Pasricha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with Computers categories.
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.