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Reram Based Machine Learning
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Book Synopsis ReRAM-based Machine Learning by : Hao Yu
Download or read book ReRAM-based Machine Learning written by Hao Yu and published by IET. This book was released on 2021-03-05 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Serving as a bridge between researchers in the computing domain and computing hardware designers, this book presents ReRAM techniques for distributed computing using IMC accelerators, ReRAM-based IMC architectures for machine learning (ML) and data-intensive applications, and strategies to map ML designs onto hardware accelerators.
Book Synopsis Processing-in-Memory for AI by : Joo-Young Kim
Download or read book Processing-in-Memory for AI written by Joo-Young Kim and published by Springer Nature. This book was released on 2022-07-09 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to processing-in-memory (PIM) technology, from its architectures to circuits implementations on multiple memory types and describes how it can be a viable computer architecture in the era of AI and big data. The authors summarize the challenges of AI hardware systems, processing-in-memory (PIM) constraints and approaches to derive system-level requirements for a practical and feasible PIM solution. The presentation focuses on feasible PIM solutions that can be implemented and used in real systems, including architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM).
Book Synopsis Built-in Fault-Tolerant Computing Paradigm for Resilient Large-Scale Chip Design by : Xiaowei Li
Download or read book Built-in Fault-Tolerant Computing Paradigm for Resilient Large-Scale Chip Design written by Xiaowei Li and published by Springer Nature. This book was released on 2023-03-01 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the end of Dennard scaling and Moore’s law, IC chips, especially large-scale ones, now face more reliability challenges, and reliability has become one of the mainstay merits of VLSI designs. In this context, this book presents a built-in on-chip fault-tolerant computing paradigm that seeks to combine fault detection, fault diagnosis, and error recovery in large-scale VLSI design in a unified manner so as to minimize resource overhead and performance penalties. Following this computing paradigm, we propose a holistic solution based on three key components: self-test, self-diagnosis and self-repair, or “3S” for short. We then explore the use of 3S for general IC designs, general-purpose processors, network-on-chip (NoC) and deep learning accelerators, and present prototypes to demonstrate how 3S responds to in-field silicon degradation and recovery under various runtime faults caused by aging, process variations, or radical particles. Moreover, we demonstrate that 3S not only offers a powerful backbone for various on-chip fault-tolerant designs and implementations, but also has farther-reaching implications such as maintaining graceful performance degradation, mitigating the impact of verification blind spots, and improving chip yield. This book is the outcome of extensive fault-tolerant computing research pursued at the State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences over the past decade. The proposed built-in on-chip fault-tolerant computing paradigm has been verified in a broad range of scenarios, from small processors in satellite computers to large processors in HPCs. Hopefully, it will provide an alternative yet effective solution to the growing reliability challenges for large-scale VLSI designs.
Book Synopsis Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing by : Sudeep Pasricha
Download or read book Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-11-01 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Analog Circuits for Machine Learning, Current/Voltage/Temperature Sensors, and High-speed Communication by : Pieter Harpe
Download or read book Analog Circuits for Machine Learning, Current/Voltage/Temperature Sensors, and High-speed Communication written by Pieter Harpe and published by Springer Nature. This book was released on 2022-03-24 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is based on the 18 tutorials presented during the 29th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, with specific contributions focusing on analog circuits for machine learning, current/voltage/temperature sensors, and high-speed communication via wireless, wireline, or optical links. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.
Book Synopsis Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications by : Tran Khanh Dang
Download or read book Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications written by Tran Khanh Dang and published by Springer Nature. This book was released on 2022-11-19 with total page 773 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Conference on Future Data and Security Engineering, FDSE 2022, held in Ho Chi Minh City, Vietnam, during November 23–25, 2022. The 41 full papers(including 4 invited keynotes) and 12 short papers included in this book were carefully reviewed and selected from 170 submissions. They were organized in topical sections as follows: invited keynotes; big data analytics and distributed systems; security and privacy engineering; machine learning and artificial intelligence for security and privacy; smart city and industry 4.0 applications; data analytics and healthcare systems; and security and data engineering.
Book Synopsis Introduction to Machine Learning in the Cloud with Python by : Pramod Gupta
Download or read book Introduction to Machine Learning in the Cloud with Python written by Pramod Gupta and published by Springer Nature. This book was released on 2021-04-28 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.
Book Synopsis Machine Learning in VLSI Computer-Aided Design by : Ibrahim (Abe) M. Elfadel
Download or read book Machine Learning in VLSI Computer-Aided Design written by Ibrahim (Abe) M. Elfadel and published by Springer. This book was released on 2019-03-15 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center
Book Synopsis Domain-Specific Computer Architectures for Emerging Applications by : Chao Wang
Download or read book Domain-Specific Computer Architectures for Emerging Applications written by Chao Wang and published by CRC Press. This book was released on 2024-06-04 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application. DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications, such as machine learning, data mining, neural networks, and graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures. Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas.
Book Synopsis Artificial Intelligence and Hardware Accelerators by : Ashutosh Mishra
Download or read book Artificial Intelligence and Hardware Accelerators written by Ashutosh Mishra and published by Springer Nature. This book was released on 2023-03-15 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores new methods, architectures, tools, and algorithms for Artificial Intelligence Hardware Accelerators. The authors have structured the material to simplify readers’ journey toward understanding the aspects of designing hardware accelerators, complex AI algorithms, and their computational requirements, along with the multifaceted applications. Coverage focuses broadly on the hardware aspects of training, inference, mobile devices, and autonomous vehicles (AVs) based AI accelerators
Book Synopsis Frontiers of Quality Electronic Design (QED) by : Ali Iranmanesh
Download or read book Frontiers of Quality Electronic Design (QED) written by Ali Iranmanesh and published by Springer Nature. This book was released on 2023-01-11 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quality Electronic Design (QED)’s landscape spans a vast region where territories of many participating disciplines and technologies overlap. This book explores the latest trends in several key topics related to quality electronic design, with emphasis on Hardware Security, Cybersecurity, Machine Learning, and application of Artificial Intelligence (AI). The book includes topics in nonvolatile memories (NVM), Internet of Things (IoT), FPGA, and Neural Networks.
Book Synopsis VLSI-SoC: Design and Engineering of Electronics Systems Based on New Computing Paradigms by : Nicola Bombieri
Download or read book VLSI-SoC: Design and Engineering of Electronics Systems Based on New Computing Paradigms written by Nicola Bombieri and published by Springer. This book was released on 2019-06-25 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains extended and revised versions of the best papers presented at the 26th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2018, held in Verona, Italy, in October 2018. The 13 full papers included in this volume were carefully reviewed and selected from the 27 papers (out of 106 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 heterogeneous, neuromorphic and brain-inspired, biologically-inspired, approximate computing systems.
Book Synopsis Compact and Fast Machine Learning Accelerator for IoT Devices by : Hantao Huang
Download or read book Compact and Fast Machine Learning Accelerator for IoT Devices written by Hantao Huang and published by Springer. This book was released on 2018-12-07 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
Download or read book NANO-CHIPS 2030 written by Boris Murmann and published by Springer Nature. This book was released on 2020-06-08 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, a global team of experts from academia, research institutes and industry presents their vision on how new nano-chip architectures will enable the performance and energy efficiency needed for AI-driven advancements in autonomous mobility, healthcare, and man-machine cooperation. Recent reviews of the status quo, as presented in CHIPS 2020 (Springer), have prompted the need for an urgent reassessment of opportunities in nanoelectronic information technology. As such, this book explores the foundations of a new era in nanoelectronics that will drive progress in intelligent chip systems for energy-efficient information technology, on-chip deep learning for data analytics, and quantum computing. Given its scope, this book provides a timely compendium that hopes to inspire and shape the future of nanoelectronics in the decades to come.
Download or read book Neuromorphic Computing written by and published by BoD – Books on Demand. This book was released on 2023-11-15 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the cutting-edge world of Neuromorphic Computing, a groundbreaking volume that unravels the secrets of brain-inspired computational paradigms. Spanning neuroscience, artificial intelligence, and hardware design, this book presents a comprehensive exploration of neuromorphic systems, empowering both experts and newcomers to embrace the limitless potential of brain-inspired computing. Discover the fundamental principles that underpin neural computation as we journey through the origins of neuromorphic architectures, meticulously crafted to mimic the brain’s intricate neural networks. Unlock the true essence of learning mechanisms – unsupervised, supervised, and reinforcement learning – and witness how these innovations are shaping the future of artificial intelligence.
Book Synopsis Transparent Data Mining for Big and Small Data by : Tania Cerquitelli
Download or read book Transparent Data Mining for Big and Small Data written by Tania Cerquitelli and published by Springer. This book was released on 2017-05-09 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches.As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.
Book Synopsis Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications by : Christos Volos
Download or read book Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications written by Christos Volos and published by Academic Press. This book was released on 2021-06-17 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling. As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields. Covers a broad range of interdisciplinary topics between mathematics, circuits, realizations, and practical applications related to nonlinear dynamical systems, nanotechnology, analog and digital systems, computer science and artificial intelligence Presents recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) Includes interesting applications of mem-elements in nonlinear dynamical systems, analog and digital systems, neuromorphic circuits, computer science and artificial intelligence
