The book covers a range of topics dealing with emerging computing technologies which are being developed in response to challenges faced due to scaling CMOS technologies. It provides a sneak peek into the capabilities unleashed by these technologies across the complete system stack, with contributions by experts discussing device technology, circuit, architecture and design automation flows. Presenting a gradual progression of the individual sub-domains and the open research and adoption challenges, this book will be of interest to industry and academic researchers, technocrats and policymakers. Chapters "Innovative Memory Architectures Using Functionality Enhanced Devices" and "Intelligent Edge Biomedical Sensors in the Internet of Things (IoT) Era" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book explores energy efficiency techniques for high-performance computing (HPC) systems using power-management methods. Adopting a step-by-step approach, it describes power-management flows, algorithms and mechanism that are employed in modern processors such as Intel Sandy Bridge, Haswell, Skylake and other architectures (e.g. ARM). Further, it includes practical examples and recent studies demonstrating how modem processors dynamically manage wide power ranges, from a few milliwatts in the lowest idle power state, to tens of watts in turbo state. Moreover, the book explains how thermal and power deliveries are managed in the context this huge power range. The book also discusses the different metrics for energy efficiency, presents several methods and applications of the power and energy estimation, and shows how by using innovative power estimation methods and new algorithms modern processors are able to optimize metrics such as power, energy, and performance. Different power estimation tools are presented, including tools that break down the power consumption of modern processors at sub-processor core/thread granularity. The book also investigates software, firmware and hardware coordination methods of reducing power consumption, for example a compiler-assisted power management method to overcome power excursions. Lastly, it examines firmware algorithms for dynamic cache resizing and dynamic voltage and frequency scaling (DVFS) for memory sub-systems.

This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn't work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.

This book introduces a novel framework for accurately modeling the errors in nanoscale CMOS technology and developing a smooth tool flow at high-level design abstractions to estimate and mitigate the effects of errors. The book presents novel techniques for high-level fault simulation and reliability estimation as well as architecture-level and system-level fault tolerant designs. It also presents a survey of state-of-the-art problems and solutions, offering insights into reliability issues in digital design and their cross-layer countermeasures.

This book explores energy efficiency techniques for high-performance computing (HPC) systems using power-management methods. Adopting a step-by-step approach, it describes power-management flows, algorithms and mechanism that are employed in modern processors such as Intel Sandy Bridge, Haswell, Skylake and other architectures (e.g. ARM). Further, it includes practical examples and recent studies demonstrating how modem processors dynamically manage wide power ranges, from a few milliwatts in the lowest idle power state, to tens of watts in turbo state. Moreover, the book explains how thermal and power deliveries are managed in the context this huge power range. The book also discusses the different metrics for energy efficiency, presents several methods and applications of the power and energy estimation, and shows how by using innovative power estimation methods and new algorithms modern processors are able to optimize metrics such as power, energy, and performance. Different power estimation tools are presented, including tools that break down the power consumption of modern processors at sub-processor core/thread granularity. The book also investigates software, firmware and hardware coordination methods of reducing power consumption, for example a compiler-assisted power management method to overcome power excursions. Lastly, it examines firmware algorithms for dynamic cache resizing and dynamic voltage and frequency scaling (DVFS) for memory sub-systems.

This book introduces a novel framework for accurately modeling the errors in nanoscale CMOS technology and developing a smooth tool flow at high-level design abstractions to estimate and mitigate the effects of errors. The book presents novel techniques for high-level fault simulation and reliability estimation as well as architecture-level and system-level fault tolerant designs. It also presents a survey of state-of-the-art problems and solutions, offering insights into reliability issues in digital design and their cross-layer countermeasures.

This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.

Increasing complexity of modern embedded systems demands system designers to ramp up their design productivity without compromising performance goals. This is promoted by modern Electronic System Level (ESL) techniques. Language-driven Exploration and Implementation of Partially Re-configurable ASIPs addresses an important segment of the ESL area by modeling partially re-configurable processors via high-level Architecture Description Language (ADL). This approach also hints an imminent evolution in the area of re-configurable system design.

Increasing complexity of modern embedded systems demands system designers to ramp up their design productivity without compromising performance goals. This is promoted by modern Electronic System Level (ESL) techniques. Language-driven Exploration and Implementation of Partially Re-configurable ASIPs addresses an important segment of the ESL area by modeling partially re-configurable processors via high-level Architecture Description Language (ADL). This approach also hints an imminent evolution in the area of re-configurable system design.

This book contains extended and revised versions of the best papers presented at the 29th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2021, held in Singapore, in October 2021*. The 12 full papers included in this volume were carefully reviewed and selected from the 44 papers (out of 75 submissions) presented at the conference. The papers discuss the latest academic and industrial results and developments as well as future trends in the field of System-on-Chip (SoC) design, considering the challenges of nano-scale, state-of-the-art and emerging manufacturing technologies. In particular they address cutting-edge research fields like low-power design of RF, analog and mixed-signal circuits, EDA tools for the synthesis and verification of heterogenous SoCs, accelerators for cryptography and deep learning and on-chip Interconnection system, reliability and testing, and integration of 3D-ICs. *The conference was held virtually.

The book covers a range of topics dealing with emerging computing technologies which are being developed in response to challenges faced due to scaling CMOS technologies. It provides a sneak peek into the capabilities unleashed by these technologies across the complete system stack, with contributions by experts discussing device technology, circuit, architecture and design automation flows. Presenting a gradual progression of the individual sub-domains and the open research and adoption challenges, this book will be of interest to industry and academic researchers, technocrats and policymakers. Chapters "Innovative Memory Architectures Using Functionality Enhanced Devices" and "Intelligent Edge Biomedical Sensors in the Internet of Things (IoT) Era" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book offers an in-depth study of the design and challenges addressed by a high-level synthesis tool targeting a specific class of cryptographic kernels, i.e. symmetric key cryptography. With the aid of detailed case studies, it also discusses optimization strategies that cannot be automatically undertaken by CRYKET (Cryptographic kernels toolkit. The dynamic nature of cryptography, where newer cryptographic functions and attacks frequently surface, means that such a tool can help cryptographers expedite the very large scale integration (VLSI) design cycle by rapidly exploring various design alternatives before reaching an optimal design option. Features include flexibility in cryptographic processors to support emerging cryptanalytic schemes; area-efficient multinational designs supporting various cryptographic functions; and design scalability on modern graphics processing units (GPUs). These case studies serve as a guide to cryptographers exploring the design of efficient cryptographic implementations.

This book constitutes the refereed proceedings of the 8th International Conference on Security, Privacy, and Applied Cryptography Engineering, SPACE 2018, held in Kanpur, India, in December 2018. The 12 full papers presented were carefully reviewed and selected from 34 submissions. This annual event is devoted to various aspects of security, privacy, applied cryptography, and cryptographic engineering. This is indeed a very challenging field, requiring the expertise from diverse domains, ranging from mathematics to solid-state circuit design.