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Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing.

machine learning --- neural networks --- gait analysis --- embedded system --- NTV --- NTC --- low-power --- low-voltage memory and clocking circuits --- minimum-energy design --- power-performance --- resilient adaptive computing --- edge devices --- power management --- energy efficiency --- near-threshold computing (NTC) --- deep neural network (DNN) --- accelerators --- timing error --- AI --- tensor processing unit (TPU) --- multiply and accumulate (MAC) --- reliability --- Near-Threshold Computing --- functional unit --- performance optimization --- cross-layer optimization

Choose an application

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

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing.

Technology: general issues --- machine learning --- neural networks --- gait analysis --- embedded system --- NTV --- NTC --- low-power --- low-voltage memory and clocking circuits --- minimum-energy design --- power-performance --- resilient adaptive computing --- edge devices --- power management --- energy efficiency --- near-threshold computing (NTC) --- deep neural network (DNN) --- accelerators --- timing error --- AI --- tensor processing unit (TPU) --- multiply and accumulate (MAC) --- reliability --- Near-Threshold Computing --- functional unit --- performance optimization --- cross-layer optimization