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 Object Recognition Using Structured Feature Extraction With A Reconfigurable Neurosynaptic Processor written by Priyanka Gomatam and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuromorphic hardware has culminated increased interest, with a focus on designing efficient platforms that can support neural network tasks. Many algorithms used in applications today extensively perform pre-processing of data, in which structured features of the data are extracted and used for classification. The idea is to explore the ability to implement a structured computation in a neuromorphic platform. This involves leveraging the operations that are best suited for neuromorphic hardware, and using them to achieve the same results as a traditional algorithm. In this paper, a case study of mapping the feature extraction stage of pedestrian detection using Histogram of Oriented Gradients (HoG) onto a neuromorphic platform is performed. Further, this neuromorphic feature extractor is then connected to a neural network based classifier. The performance of the feature extraction done by a 1:1 mapping of the algorithm is evaluated against other neuromorphic implementations, as well as an FPGA implementation. The neuromorphic platform chosen for this experiment is IBMs TrueNorth, a Neurosynaptic System.

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 In Vitro Neuronal Networks written by Michela Chiappalone and published by Springer. This book was released on 2019-05-09 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the incredible advances achieved in the study of in vitro neuronal networks for use in basic and applied research. These cultures of dissociated neurons offer a perfect trade-off between complex experimental models and theoretical modeling approaches giving new opportunities for experimental design but also providing new challenges in data management and interpretation. Topics include culturing methodologies, neuroengineering techniques, stem cell derived neuronal networks, techniques for measuring network activity, and recent improvements in large-scale data analysis. The book ends with a series of case studies examining potential applications of these technologies.

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 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 Spike based learning application for neuromorphic engineering written by Anup Das and published by Frontiers Media SA. This book was released on 2024-08-22 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spiking Neural Networks (SNN) closely imitate biological networks. Information processing occurs in both spatial and temporal manner, making SNN extremely interesting for the pertinent mimicking of the biological brain. Biological brains code and transmit the sensory information in the form of spikes that capture the spatial and temporal information of the environment with amazing precision. This information is processed in an asynchronous way by the neural layer performing recognition of complex spatio-temporal patterns with sub-milliseconds delay and at with a power budget in the order of 20W. The efficient spike coding mechanism and the asynchronous and sparse processing and communication of spikes seems to be key in the energy efficiency and high-speed computation capabilities of biological brains. SNN low-power and event-based computation make them more attractive when compared to other artificial neural networks (ANN).

Download or read book Deep In memory Architectures for Machine Learning written by Mingu Kang and published by Springer Nature. This book was released on 2020-01-30 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Spintronics written by Kaiyou Wang and published by John Wiley & Sons. This book was released on 2022-07-14 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest advances in spintronic materials, devices, and applications In Spintronics: Materials, Devices and Applications, a team of distinguished researchers delivers a holistic introduction to spintronic effects within cutting-edge materials and applications. Containing the perfect balance of academic research and practical application, the book discusses the potential—and the key limitations and challenges—of spintronic devices. The latest title in the Wiley Series in Materials for Electronic and Optoelectronic Applications, Spintronics: Materials, Devices and Applications explores giant magneto-resistance (GMR) and tunneling magnetic resistance (TMR) materials, spin-transfer torque and spin-orbit torque materials, spin oscillators, and spin materials for use in artificial neural networks. Applications in multi-ferroelectric and antiferromagnetic materials are presented as well. This book also includes: A thorough introduction to recent research developments in the fields of spintronic materials, devices, and applications Comprehensive explorations of skymions, magnetic semiconductors, and antiferromagnetic materials Practical discussions of spin-transfer torque materials and devices for magnetic random-access memory In-depth examinations of giant magneto-resistance materials and devices for magnetic sensors Perfect for advanced students and researchers in materials science, physics, electronics, and computer science, Spintronics: Materials, Devices and Applications will also earn a place in the libraries of professionals working in the manufacture of optics, photonics, and nanometrology equipment.

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 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 Recent Advances and Emerging Challenges in STEM written by Yadir Torres and published by Springer Nature. This book was released on with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Emerging Technologies and Systems for Biologically Plausible Implementations of Neural Functions written by Erika Covi and published by Frontiers Media SA. This book was released on 2022-04-26 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 2023-03-21 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 TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Download or read book Towards Ubiquitous Low power Image Processing Platforms written by Magnus Jahre and published by Springer Nature. This book was released on 2020-12-15 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the key scientific outcomes of the Horizon 2020 research project TULIPP: Towards Ubiquitous Low-power Image Processing Platforms. The main focus lies on the development of high-performance, energy-efficient embedded systems for the growing range of increasingly complex image processing applications. The holistic TULIPP approach is described in the book, which addresses hardware platforms, programming tools and embedded operating systems. Several of the results are available as open-source hardware/software for the community. The results are evaluated with several use cases taken from real-world applications in key domains such as Unmanned Aerial Vehicles (UAVs), robotics, space and medicine. Discusses the development of high-performance, energy-efficient embedded systems for the growing range of increasingly complex image processing applications; Covers the hardware architecture of embedded image processing systems, novel methods, tools and libraries for programming those systems as well as embedded operating systems to manage those systems; Demonstrates results with several challenging applications, such as medical systems, robotics, drones and automotive.

Download or read book Biohybrid Systems written by Ranu Jung and published by John Wiley & Sons. This book was released on 2011-11-30 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: The discipline of neurodesign is a highly interdisciplinary one, while at the same time in the process of maturing towards real-life applications. The breakthrough about to be achieved is to close the loop in communication between neural systems and electronic and mechatronic systems and actually let the nervous system adapt to the feedback from the man-made systems. To master this loop, scientists need a sound understanding of neurology, from the cellular to the systems scale, of man-made systems and how to connect the two. These scientists comprise medical scientists, neurologists and physiologists, engineers, as well as biophysicists. And they need the topics in a coherently written work with chapters building upon another.