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,...

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.

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...

This book provides its readers with the means to implement energy-efficient video systems, by using different optimization approaches at multiple abstraction levels.

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.

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.

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...