Download or read book Reconfigurable Accelerators for Neuromorphic Systems written by Dharav J. Dantara and published by . This book was released on 2011 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Event Based Neuromorphic Systems written by Shih-Chii Liu and published by John Wiley & Sons. This book was released on 2015-02-16 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuromorphic electronic engineering takes its inspiration from the functioning of nervous systems to build more power efficient electronic sensors and processors. Event-based neuromorphic systems are inspired by the brain's efficient data-driven communication design, which is key to its quick responses and remarkable capabilities. This cross-disciplinary text establishes how circuit building blocks are combined in architectures to construct complete systems. These include vision and auditory sensors as well as neuronal processing and learning circuits that implement models of nervous systems. Techniques for building multi-chip scalable systems are considered throughout the book, including methods for dealing with transistor mismatch, extensive discussions of communication and interfacing, and making systems that operate in the real world. The book also provides historical context that helps relate the architectures and circuits to each other and that guides readers to the extensive literature. Chapters are written by founding experts and have been extensively edited for overall coherence. This pioneering text is an indispensable resource for practicing neuromorphic electronic engineers, advanced electrical engineering and computer science students and researchers interested in neuromorphic systems. Key features: Summarises the latest design approaches, applications, and future challenges in the field of neuromorphic engineering. Presents examples of practical applications of neuromorphic design principles. Covers address-event communication, retinas, cochleas, locomotion, learning theory, neurons, synapses, floating gate circuits, hardware and software infrastructure, algorithms, and future challenges.
Download or read book Neuromorphic Photonic Devices and Applications written by Min Gu and published by Elsevier. This book was released on 2023-12-01 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuromorphic Photonic Devices and Applications synthesizes the most critical advances in photonic neuromorphic models, photonic material platforms and accelerators for neuromorphic computing. The book discusses fields and applications that can leverage these new platforms. A brief review of the historical development of the field is followed by a discussion of the emerging 2D photonic materials platforms and recent work in implementing neuromorphic models and 3D neuromorphic systems. The application of artificial intelligence (AI), such as neuromorphic models to inverse design neuromorphic materials and devices and predict performance challenges is discussed throughout. Finally, a comprehensive overview of the applications of neuromorphic photonic technologies and the challenges, opportunities and future prospects is discussed, making the book suitable for researchers and practitioners in academia and R&D in the multidisciplinary field of photonics. - Includes overview of primary scientific concepts for the research topic of neuromorphic photonics such as neurons as computational units, artificial intelligence, machine learning and neuromorphic models - Reviews the latest advances in photonic materials, device platforms and enabling technology drivers of neuromorphic photonics - Discusses potential applications in computing and optical communications
Download or read book Artificial Intelligence Applications and Reconfigurable Architectures written by Anuradha D. Thakare and published by John Wiley & Sons. This book was released on 2023-02-14 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE APPLICATIONS and RECONFIGURABLE ARCHITECTURES The primary goal of this book is to present the design, implementation, and performance issues of AI applications and the suitability of the FPGA platform. This book covers the features of modern Field Programmable Gate Arrays (FPGA) devices, design techniques, and successful implementations pertaining to AI applications. It describes various hardware options available for AI applications, key advantages of FPGAs, and contemporary FPGA ICs with software support. The focus is on exploiting parallelism offered by FPGA to meet heavy computation requirements of AI as complete hardware implementation or customized hardware accelerators. This is a comprehensive textbook on the subject covering a broad array of topics like technological platforms for the implementation of AI, capabilities of FPGA, suppliers’ software tools and hardware boards, and discussion of implementations done by researchers to encourage the AI community to use and experiment with FPGA. Readers will benefit from reading this book because It serves all levels of students and researcher’s as it deals with the basics and minute details of Ecosystem Development Requirements for Intelligent applications with reconfigurable architectures whereas current competitors’ books are more suitable for understanding only reconfigurable architectures. It focuses on all aspects of machine learning accelerators for the design and development of intelligent applications and not on a single perspective such as only on reconfigurable architectures for IoT applications. It is the best solution for researchers to understand how to design and develop various AI, deep learning, and machine learning applications on the FPGA platform. It is the best solution for all types of learners to get complete knowledge of why reconfigurable architectures are important for implementing AI-ML applications with heavy computations. Audience Researchers, industrial experts, scientists, and postgraduate students who are working in the fields of computer engineering, electronics, and electrical engineering, especially those specializing in VLSI and embedded systems, FPGA, artificial intelligence, Internet of Things, and related multidisciplinary projects.
Download or read book A Reconfigurable Accelerator For Neuromorphic Object Recognition written by Jagdish Sabarad and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant challenge in creating machines with artificial vision is designing systems which can process visual information as efficiently as the human brain. Recent advances in neuroscience have enabled researchers to develop computational models of auditory, visual and learning perceptions in the human brain. Among these models, the two widely accepted algorithms that model the process of attention and recognition in the mammalian visual pathway are - the Saliency based model for visual attention and HMAX model for object recognition. One of the major burdens of these biologically plausible models is their massive computational demands. Real time implemen- tation of these biologically inspired vision algorithms, while challenging, can have a diverse and profound impact in applications like autonomous vehicle navigation, surveillance, robotics and face, text and gesture recognition. To mimic true biological systems, implementations of these algorithms must not only meet real-time performance goals, but also stringent power budgets and small form-factors. Previous attempts to parallelize the HMAX model on multi-core processors have been unable to provide real-time performance due to limited parallelism and high computational complexity. Researchers have leveraged graphics processors due to their ease of programmability and high parallelism. However, their excessive power consumption hinders deployment in embedded or low-power systems. The focus of this work is on the design and architecture of a reconfigurable hardware acceler- ator for the time consuming S2-C2 stage of the HMAX model. The accelerator leverages spatial parallelism, dedicated wide data buses with on-chip memories to provide an energy efficient solution to enable adoption into embedded systems. This work presents a systolic array-based architecture which includes a run-time reconfigurable convolution engine which can perform mul- tiple variable-sized convolutions in parallel. An automation flow is described for this accelerator which can generate optimal hardware configurations for a given algorithmic specification and also perform run-time configuration and execution seamlessly. Experimental results on Virtex-6 FPGA platforms show 5X to 11X speedups and 14X to 33X higher performance-per-Watt over a CNS-based implementation on a Tesla GPU.
Download or read book Neuromorphic Computing Principles and Organization written by Abderazek Ben Abdallah and published by Springer Nature. This book was released on 2022-05-31 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given. A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well. Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.
Download or read book Applied Reconfigurable Computing Architectures Tools and Applications written by Francesca Palumbo and published by Springer Nature. This book was released on 2023-09-15 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 19th International Symposium on Applied Reconfigurable Computing, ARC 2023, which was held in Cottbus, Germany, in September 2023. The 18 full papers presented in this volume were reviewed and selected from numerous submissions. The proceedings also contain 4 short PhD papers. The contributions were organized in topical sections as follows: Design methods and tools; applications; architectures; special session: near and in-memory computing; and PhD forum papers.
Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by Shiho Kim and published by Elsevier. This book was released on 2021-04-07 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
Download or read book Neuromorphic Engineering Systems and Applications written by André van Schaik and published by Frontiers Media SA. This book was released on 2015-07-05 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuromorphic engineering has just reached its 25th year as a discipline. In the first two decades neuromorphic engineers focused on building models of sensors, such as silicon cochleas and retinas, and building blocks such as silicon neurons and synapses. These designs have honed our skills in implementing sensors and neural networks in VLSI using analog and mixed mode circuits. Over the last decade the address event representation has been used to interface devices and computers from different designers and even different groups. This facility has been essential for our ability to combine sensors, neural networks, and actuators into neuromorphic systems. More recently, several big projects have emerged to build very large scale neuromorphic systems. The Telluride Neuromorphic Engineering Workshop (since 1994) and the CapoCaccia Cognitive Neuromorphic Engineering Workshop (since 2009) have been instrumental not only in creating a strongly connected research community, but also in introducing different groups to each other’s hardware. Many neuromorphic systems are first created at one of these workshops. With this special research topic, we showcase the state-of-the-art in neuromorphic systems.
Download or read book Neuromorphic Photonics written by Paul R. Prucnal and published by CRC Press. This book was released on 2017-05-08 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.
Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by and published by Academic Press. This book was released on 2021-03-28 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. - Updates on new information on the architecture of GPU, NPU and DNN - Discusses In-memory computing, Machine intelligence and Quantum computing - Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
Download or read book Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems written by Ali Safa and published by Springer Nature. This book was released on with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Contributions to Neuromorphic and Reconfigurable Circuits and Systems written by Stephen Howard Nease and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a body of work in the field of reconfigurable and neuromorphic circuits and systems. Three main projects were undertaken. The first was using a Field-Programmable Analog Array (FPAA) to model the cable behavior of dendrites using analog circuits. The second was to design, lay out, and test part of a new FPAA, the RASP 2.9v. The final project was to use floating-gate programming to remove offsets in a neuromorphic FPAA, the RASP Neuron 1D.
Download or read book Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning written by Lei Deng and published by Frontiers Media SA. This book was released on 2021-05-05 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Reconfigurable Accelerators in the World of General purpose Computing written by Tobias Kenter and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applied Reconfigurable Computing Architectures Tools and Applications written by Iouliia Skliarova and published by Springer Nature. This book was released on with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt:
