Processing-in-Memory for AI - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Processing-in-Memory for AI write by Joo-Young Kim. This book was released on 2022-07-09. Processing-in-Memory for AI available in PDF, EPUB and Kindle. 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).
From Artificial Intelligence to Brain Intelligence
From Artificial Intelligence to Brain Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook From Artificial Intelligence to Brain Intelligence write by Rajiv Joshi. This book was released on 2022-09-01. From Artificial Intelligence to Brain Intelligence available in PDF, EPUB and Kindle. Research in Artificial Intelligence (AI) is not new, it has been around since 1950’s. AI resurfaced at that time while Moore’s law was on an aggressive path of scaling, with the transformation of NMOS and later bipolar technology to CMOS for high performance, low power as well as low cost applications.Several breakthroughs in the electronics industry helped to push Moore’s law in chip miniaturization along with increased computing power (parallel and distributed processing) and memory bandwidth. Once this paradigm shift occurred it naturally opened doors for AI as it required big data manipulations, and thus AI could thrive again. AI has already shown success in industries such as finance, marketing, health care, transportation, gaming, education and the defence and space, to name but a few.The human brain amazingly has a memory in the order of millions of digital bits, however it cannot compete with machines for data crunching and speed. Thus tomorrow’s world will be a World of Wonders of Artificial Intelligence (WOW- AI), to compensate the computational limitations of human beings. In short, AI research and applications will continue to grow with the development of software, algorithms and hardware accelerators.To continue the development of AI, an advanced AI Compute Symposium was launched with the sponsorship of IBM, IEEE CAS and EDS, from which this book came. Overall, the book covers two broad topics: general AI advances, and applications to neuromorphic computing.
Efficient Processing of Deep Neural Networks
Efficient Processing of Deep Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Processing of Deep Neural Networks write by Vivienne Sze. This book was released on 2022-05-31. Efficient Processing of Deep Neural Networks available in PDF, EPUB and Kindle. This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Deep In-memory Architectures for Machine Learning
Deep In-memory Architectures for Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep In-memory Architectures for Machine Learning write by Mingu Kang. This book was released on 2020-01-30. Deep In-memory Architectures for Machine Learning available in PDF, EPUB and Kindle. This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware.
In-/Near-Memory Computing
In-/Near-Memory Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook In-/Near-Memory Computing write by Daichi Fujiki. This book was released on 2021-08-12. In-/Near-Memory Computing available in PDF, EPUB and Kindle. This book provides a structured introduction of the key concepts and techniques that enable in-/near-memory computing. For decades, processing-in-memory or near-memory computing has been attracting growing interest due to its potential to break the memory wall. Near-memory computing moves compute logic near the memory, and thereby reduces data movement. Recent work has also shown that certain memories can morph themselves into compute units by exploiting the physical properties of the memory cells, enabling in-situ computing in the memory array. While in- and near-memory computing can circumvent overheads related to data movement, it comes at the cost of restricted flexibility of data representation and computation, design challenges of compute capable memories, and difficulty in system and software integration. Therefore, wide deployment of in-/near-memory computing cannot be accomplished without techniques that enable efficient mapping of data-intensive applications to such devices, without sacrificing accuracy or increasing hardware costs excessively. This book describes various memory substrates amenable to in- and near-memory computing, architectural approaches for designing efficient and reliable computing devices, and opportunities for in-/near-memory acceleration of different classes of applications.
