Model Optimization Methods For Efficient And Edge Ai

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Model Optimization Methods For Efficient And Edge Ai

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Author : Pethuru Raj Chelliah
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-13

Model Optimization Methods For Efficient And Edge Ai written by Pethuru Raj Chelliah 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 2024-11-13 with Computers categories.

Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.

Advances In Smart Knowledge Computing

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Author : Ford Lumban Gaol
language : en
Publisher: Springer Nature
Release Date : 2025-11-28

Advances In Smart Knowledge Computing written by Ford Lumban Gaol and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-28 with Computers categories.

This book explores the evolution of computing beyond current AI paradigms, with a particular emphasis on the potential of knowledge to be used to develop more intelligent, adaptive, and potentially conscious systems. This book has three elements: First, moving beyond "Narrow AI" and data-centric paradigms were investigated.The book likely discusses the inherent limitations of current "narrow AI", despite impressive performance in specific tasks, interpretability, reasoning capabilities, and true understanding. Second, the modelling and uses cases were explored. The book would explore advanced methods for representing knowledge and performing logical or probabilistic reasoning over that knowledge. Third, provide the applications and impact of post-AI. The book provides with details of smart knowledge computing which can lead to more informed, explainable, and robust decision-making in complex real-world scenarios. In essence, this presents itself as a forward-looking investigation into how intelligence might be constructed by combining the capacity of data processing with explicit knowledge representation and advanced reasoning.

Deci Ai Model Optimization Techniques

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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-20

Deci Ai Model Optimization Techniques written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-20 with Computers categories.

"Deci AI Model Optimization Techniques" Delve into the world of efficient deep learning with "Deci AI Model Optimization Techniques," a comprehensive exploration of modern strategies for maximizing AI model performance in real-world applications. This book offers a thorough foundation in the guiding principles of model optimization, blending theoretical underpinnings with application-driven best practices. Readers are introduced to core topics such as optimization objectives, model efficiency metrics, neural network compression, and the systemic constraints that arise when deploying AI across domains like vision, NLP, and recommendation systems. Balancing depth and clarity, the text demystifies advanced subjects including automated neural architecture search (NAS), pruning, quantization, and knowledge distillation. Readers learn to harness Deci AI’s cutting-edge AutoNAC engine for multi-objective optimization, adapt models for both edge and cloud environments, and combine multiple optimization techniques for truly efficient AI pipelines. The book guides practitioners through practical design considerations—such as hardware-aware optimization, hyperparameter tuning, and integration with MLOps workflows—while equipping them with robust benchmarking, profiling, and validation protocols to ensure reliability and reproducibility. Looking toward the horizon, "Deci AI Model Optimization Techniques" addresses emerging frontiers in automated model design, federated and distributed optimization, continual learning, and the ethical challenges at the heart of responsible AI deployment. Whether you are an engineer, researcher, or technical leader seeking to deploy scalable, high-performance AI, this book provides actionable insights and future-ready solutions for building and maintaining state-of-the-art models in an ever-evolving landscape.

Optimizing Ai Models

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Author : Rosey Press
language : en
Publisher: Independently Published
Release Date : 2024-07-15

Optimizing Ai Models written by Rosey Press and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-15 with Computers categories.

Model optimization is a crucial aspect of AI development, especially in today's landscape where the costs and availability of GPUs are becoming increasingly challenging. With the rising demand for AI capabilities, there is a need to make models smaller and more efficient to reduce the dependency on large-scale infrastructure. Techniques like Low-Rank Adaptation (LoRA) and Quantization are playing a significant role in achieving this goal, allowing smaller players and startups to access sophisticated AI capabilities without breaking the bank. One of the key benefits of model optimization is the development of low-power AI models for edge computing. These models are designed to run efficiently on devices with limited computational resources, making them ideal for applications where real-time processing is essential. By optimizing AI models for edge computing, developers can ensure that their solutions are accessible and usable in a wide range of scenarios, from smart homes to industrial automation. Quantization techniques are another important tool in the model optimization toolbox, as they help reduce the size of AI models without sacrificing performance. By quantizing the parameters of a model, developers can significantly reduce the amount of memory required to store and run the model, making it more accessible for deployment on a variety of devices. This is particularly important for applications where storage and memory constraints are a concern, such as mobile devices and IoT devices. Compression algorithms are also a key component of model optimization, as they enable efficient storage and transfer of AI models. By compressing the parameters of a model, developers can reduce the overall size of the model without compromising its performance, making it easier to deploy and manage in a variety of environments. This is particularly important for applications where bandwidth and storage space are limited, such as in healthcare applications and autonomous vehicles. Overall, model optimization plays a crucial role in making AI more accessible and efficient for a wide range of applications. By leveraging techniques like Low-Rank Adaptation, Quantization, and Compression, developers can create resource-efficient AI models that are suitable for deployment on a variety of devices and platforms. Whether it's for edge computing, mobile devices, IoT devices, or real-time applications, optimizing AI models is essential for ensuring that AI technology remains accessible and usable for all.

Cornell University Courses Of Study

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Author : Cornell University
language : en
Publisher:
Release Date : 2007

Cornell University Courses Of Study written by Cornell University and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Universities and colleges categories.

Aiaa Journal

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Author : American Institute of Aeronautics and Astronautics
language : en
Publisher:
Release Date : 2003

Aiaa Journal written by American Institute of Aeronautics and Astronautics and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Aeronautics categories.

Computational Intelligence

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Author :
language : en
Publisher:
Release Date : 1987

Computational Intelligence written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Artificial intelligence categories.

Ultimate Onnx For Deep Learning Optimization Design Optimize And Deploy Deep Learning Models Using Onnx For Scalable Production And Edge Ai Systems

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Author : Meet Patel
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-12-29

Ultimate Onnx For Deep Learning Optimization Design Optimize And Deploy Deep Learning Models Using Onnx For Scalable Production And Edge Ai Systems written by Meet Patel and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-29 with Computers categories.

Bringing Deep Learning Models to the Edge Efficiently Using ONNX. Key Features● Master end-to-end ONNX workflows from framework export models to edge deployment.● Hands-on optimization techniques like quantization, pruning and knowledge distillation for real-world edge AI performance.● Production-grade case studies across vision, speech, and language models on edge devices. Book DescriptionONNX has emerged as the de facto standard for deploying portable, framework-agnostic machine learning models across diverse hardware platforms. Ultimate ONNX for Deep Learning Optimization provides a structured, end-to-end guide to the ONNX ecosystem, starting with ONNX fundamentals, model representation, and framework integration. You will learn how to export models from PyTorch, TensorFlow, and Scikit-Learn, inspect and modify ONNX graphs, and leverage ONNX Runtime and ONNX Simplifier for inference optimization. Each chapter builds technical depth, equipping you with the tools required to move models beyond experimentation. The book focuses on performance-critical optimization techniques, including quantization, pruning, and knowledge distillation, followed by practical deployment on edge devices such as Raspberry Pi. Through complete, real-world case studies covering object detection, speech recognition, and compact language models, you can implement custom operators, follow deployment best practices, and understand production constraints. Thus, by the end of this book, you will be capable of designing, optimizing, and deploying efficient ONNX-based AI systems for edge environments. What you will learn● Design and understand ONNX models, graphs, operators, and runtimes.● Convert and integrate models from PyTorch, TensorFlow, and Scikit-Learn.● Optimize inference using graph simplification, quantization, and pruning.● Apply knowledge distillation to retain accuracy on constrained devices.● Deploy and benchmark ONNX models on Raspberry Pi and edge hardware.● Build custom ONNX operators, and extend models beyond standard layers.

The Engineering Index Annual

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Author :
language : en
Publisher:
Release Date : 1992

The Engineering Index Annual written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Engineering categories.

Since its creation in 1884, Engineering Index has covered virtually every major engineering innovation from around the world. It serves as the historical record of virtually every major engineering innovation of the 20th century. Recent content is a vital resource for current awareness, new production information, technological forecasting and competitive intelligence. The world?s most comprehensive interdisciplinary engineering database, Engineering Index contains over 10.7 million records. Each year, over 500,000 new abstracts are added from over 5,000 scholarly journals, trade magazines, and conference proceedings. Coverage spans over 175 engineering disciplines from over 80 countries. Updated weekly.

Journal Of Machinery Manufacture And Reliability

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Author :
language : en
Publisher:
Release Date : 1990

Journal Of Machinery Manufacture And Reliability written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Machinery categories.