Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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. Presents an overview of the approximate arithmetic building blocks that can be used for designing power/performance efficient computing units; Discusses effective memory approximation techniques to employ in conventional, i.e., DRAM and SRAM, as well as emerging, i.e., PCM and STT-RAM, memory technologies, for improving performance, power, and/or energy efficiency of the memory for error resilient applications; Includes an overview of hardware or software/hardware approximation techniques that operate across entire computing devices, including processors, graphical processors, and accelerators that can form a SoC with processors.

Computer algorithms. --- Embedded computer systems. --- Approximation theory. --- Systems engineering. --- Computer science. --- Electronics. --- Circuits and Systems. --- Processor Architectures. --- Electronics and Microelectronics, Instrumentation. --- Electrical engineering --- Physical sciences --- Informatics --- Science --- Engineering systems --- System engineering --- Engineering --- Industrial engineering --- System analysis --- Design and construction --- Electronic circuits. --- Microprocessors. --- Microelectronics. --- Minicomputers --- Electron-tube circuits --- Electric circuits --- Electron tubes --- Electronics --- Microminiature electronic equipment --- Microminiaturization (Electronics) --- Microtechnology --- Semiconductors --- Miniature electronic equipment

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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 computer systems. --- Electronic circuits. --- Cooperating objects (Computer systems). --- Embedded Systems. --- Electronic Circuits and Systems. --- Cyber-Physical Systems.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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. Presents an overview of the approximate arithmetic building blocks that can be used for designing power/performance efficient computing units; Discusses effective memory approximation techniques to employ in conventional, i.e., DRAM and SRAM, as well as emerging, i.e., PCM and STT-RAM, memory technologies, for improving performance, power, and/or energy efficiency of the memory for error resilient applications; Includes an overview of hardware or software/hardware approximation techniques that operate across entire computing devices, including processors, graphical processors, and accelerators that can form a SoC with processors.

Electronics --- Electrical engineering --- Applied physical engineering --- Computer science --- Computer architecture. Operating systems --- computers --- elektronica --- ingenieurswetenschappen --- computerkunde --- architectuur (informatica) --- elektrische circuits

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Computer architecture. Operating systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- embedded systems --- algoritmen --- AI (artificiële intelligentie)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Electrical engineering --- Computer architecture. Operating systems --- Computer. Automation --- embedded systems --- algoritmen --- elektrische circuits