Muhammad Shafique – författare
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20 produkter
20 produkter
Inbunden, Engelska, 2024
1 667 kr
Skickas inom 10-15 vardagar
Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals.This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems.This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
- Nyhet
Häftad, Engelska, 2026
748 kr
Skickas inom 10-15 vardagar
Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals.This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems.This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
Inbunden, Engelska, 2011
1 082 kr
Skickas inom 10-15 vardagar
This book presents techniques for energy reduction in adaptive embedded multimedia systems, based on dynamically reconfigurable processors. The approach described will enable designers to meet performance/area constraints, while minimizing video quality degradation, under various, run-time scenarios. Emphasis is placed on implementing power/energy reduction at various abstraction levels. To enable this, novel techniques for adaptive energy management at both processor architecture and application architecture levels are presented, such that both hardware and software adapt together, minimizing overall energy consumption under unpredictable, design-/compile-time scenarios.
Inbunden, Engelska, 2013
1 082 kr
Skickas inom 10-15 vardagar
This book shows readers how to develop energy-efficient algorithms and hardware architectures to enable high-definition 3D video coding on resource-constrained embedded devices. Users of the Multiview Video Coding (MVC) standard face the challenge of exploiting its 3D video-specific coding tools for increasing compression efficiency at the cost of increasing computational complexity and, consequently, the energy consumption. This book enables readers to reduce the multiview video coding energy consumption through jointly considering the algorithmic and architectural levels. Coverage includes an introduction to 3D videos and an extensive discussion of the current state-of-the-art of 3D video coding, as well as energy-efficient algorithms for 3D video coding and energy-efficient hardware architecture for 3D video coding.
Häftad, Engelska, 2014
1 212 kr
Skickas inom 10-15 vardagar
This book presents techniques for energy reduction in adaptive embedded multimedia systems, based on dynamically reconfigurable processors. The approach described will enable designers to meet performance/area constraints, while minimizing video quality degradation, under various, run-time scenarios. Emphasis is placed on implementing power/energy reduction at various abstraction levels. To enable this, novel techniques for adaptive energy management at both processor architecture and application architecture levels are presented, such that both hardware and software adapt together, minimizing overall energy consumption under unpredictable, design-/compile-time scenarios.
Häftad, Engelska, 2016
1 082 kr
Skickas inom 10-15 vardagar
This book shows readers how to develop energy-efficient algorithms and hardware architectures to enable high-definition 3D video coding on resource-constrained embedded devices. Users of the Multiview Video Coding (MVC) standard face the challenge of exploiting its 3D video-specific coding tools for increasing compression efficiency at the cost of increasing computational complexity and, consequently, the energy consumption. This book enables readers to reduce the multiview video coding energy consumption through jointly considering the algorithmic and architectural levels. Coverage includes an introduction to 3D videos and an extensive discussion of the current state-of-the-art of 3D video coding, as well as energy-efficient algorithms for 3D video coding and energy-efficient hardware architecture for 3D video coding.
Häftad, Engelska, 2019
1 694 kr
Skickas inom 5-8 vardagar
Specifically, one part of the book focuses on maximizing/optimizing computational performance under power or thermal constraints, while another part focuses on minimizing energy consumption under performance (or real-time) constraints.
Inbunden, Engelska, 2023
1 297 kr
Skickas inom 10-15 vardagar
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.
Häftad, Engelska, 2024
921 kr
Skickas inom 10-15 vardagar
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.
Inbunden, Engelska, 2023
2 156 kr
Skickas inom 10-15 vardagar
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.
Häftad, Engelska, 2024
1 512 kr
Skickas inom 10-15 vardagar
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.
Inbunden, Engelska, 2023
2 156 kr
Skickas inom 10-15 vardagar
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.
Häftad, Engelska, 2024
1 512 kr
Skickas inom 10-15 vardagar
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.
Inbunden, Engelska, 2016
544 kr
Skickas inom 10-15 vardagar
This book describes novel software concepts to increase reliability under user-defined constraints. The authors’ approach bridges, for the first time, the reliability gap between hardware and software. Readers will learn how to achieve increased soft error resilience on unreliable hardware, while exploiting the inherent error masking characteristics and error (stemming from soft errors, aging, and process variations) mitigations potential at different software layers.
Inbunden, Engelska, 2017
1 082 kr
Skickas inom 10-15 vardagar
This book provides its readers with the means to implement energy-efficient video systems, by using different optimization approaches at multiple abstraction levels. The authors evaluate the complete video system with a motive to optimize its different software and hardware components in synergy, increase the throughput-per-watt, and address reliability issues. Subsequently, this book provides algorithmic and architectural enhancements, best practices and deployment models for new video systems, while considering new implementation paradigms of hardware accelerators, parallelism for heterogeneous multi- and many-core systems, and systems with long life-cycles. Particular emphasis is given to the current video encoding industry standard H.264/AVC, and one of the latest video encoders (High Efficiency Video Coding, HEVC).
Inbunden, Engelska, 2018
1 512 kr
Skickas inom 10-15 vardagar
This book focuses on two of the most relevant problems related to power management on multicore and manycore systems. Specifically, one part of the book focuses on maximizing/optimizing computational performance under power or thermal constraints, while another part focuses on minimizing energy consumption under performance (or real-time) constraints.
Häftad, Engelska, 2018
544 kr
Skickas inom 10-15 vardagar
Readers will learn how to achieve increased soft error resilience on unreliable hardware, while exploiting the inherent error masking characteristics and error (stemming from soft errors, aging, and process variations) mitigations potential at different software layers.
Häftad, Engelska, 2018
1 619 kr
Skickas inom 10-15 vardagar
This book provides its readers with the means to implement energy-efficient video systems, by using different optimization approaches at multiple abstraction levels.
Inbunden, Engelska, 2018
544 kr
Skickas inom 10-15 vardagar
This book provides readers with a comprehensive, state-of-the-art overview of approximate computing, enabling the design trade-off of accuracy for achieving better power/performance efficiencies, through the simplification of underlying computing resources. The authors describe in detail various efforts to generate approximate hardware systems, while still providing an overview of support techniques at other computing layers. The book is organized by techniques for various hardware components, from basic building blocks to general circuits and systems.
Inbunden, Engelska, 2026
2 981 kr
Kommande
This book studies in detail the robustness of machine learning (ML) algorithms involved in dealing with vulnerabilities where the errors or malfunctions are both intentional and malicious, therefore being associated with a specific attack model. Reliability is key to the wider adoption of machine learning algorithms in driving regular tasks. There needs to be guaranteed on the success of ML-driven decision-making systems, without errors. It is often seen that an otherwise typically high-performance neural network trained for a specific task, fails under certain circumstances. These vulnerabilities are a key deterrent to reliability and must be addressed before the ubiquitous adoption of AI.From the machine learning standpoint, this book looks at both critical ingredients, that is the model (neural architecture and its properties) and the training data and from the perspective of Robust AI, the investigation pertains to both Security and Privacy issues. To elaborate on the nomenclature, the Security aspects involve attacks that concern the disruption of the intended machine learning task itself. The Privacy aspect deals with attacks that pertain to leaking sensitive information or IP. A combination of both is necessary to have robust algorithms that can withstand malicious adversaries. The ideas are well described with respect to the available literature and the propositions are studied extensively with many different use cases, on multiple neural architectures and datasets. The content of this book caters to researchers, programmers, engineering, and policymakers who are interested in the implementation of Robust AI and its security and privacy issues in machine learning.