Fpga Based Hardware Acceleration For Brain State In A Box Models In Neoromorphic Computing

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Emerging Technology And Architecture For Big Data Analytics

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Author : Anupam Chattopadhyay
language : en
Publisher: Springer
Release Date : 2017-04-19

Emerging Technology And Architecture For Big Data Analytics written by Anupam Chattopadhyay and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-19 with Technology & Engineering categories.

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.

Thinking Machines

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Author : Shigeyuki Takano
language : en
Publisher: Academic Press
Release Date : 2021-03-27

Thinking Machines written by Shigeyuki Takano and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-27 with Computers categories.

Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the latest trends and examples of machine learning hardware and basic knowledge of machine learning in general, but also the main issues involved in its implementation. Readers will learn what is required for the design of machine learning hardware for neuromorphic computing and/or neural networks.This is a recommended book for those who have basic knowledge of machine learning or those who want to learn more about the current trends of machine learning. - Presents a clear understanding of various available machine learning hardware accelerator solutions that can be applied to selected machine learning algorithms - Offers key insights into the development of hardware, from algorithms, software, logic circuits, to hardware accelerators - Introduces the baseline characteristics of deep neural network models that should be treated by hardware as well - Presents readers with a thorough review of past research and products, explaining how to design through ASIC and FPGA approaches for target machine learning models - Surveys current trends and models in neuromorphic computing and neural network hardware architectures - Outlines the strategy for advanced hardware development through the example of deep learning accelerators

Fpga Based Hardware Acceleration For Brain State In A Box Models In Neoromorphic Computing

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Author : Siva Aneesh Gadela
language : en
Publisher: ProQuest
Release Date : 2008

Fpga Based Hardware Acceleration For Brain State In A Box Models In Neoromorphic Computing written by Siva Aneesh Gadela and has been published by ProQuest this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Artificial intelligence categories.

Fpga Based Hardware Accelerators

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Author : Iouliia Skliarova
language : en
Publisher: Springer
Release Date : 2019-05-30

Fpga Based Hardware Accelerators written by Iouliia Skliarova and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Technology & Engineering categories.

This book suggests and describes a number of fast parallel circuits for data/vector processing using FPGA-based hardware accelerators. Three primary areas are covered: searching, sorting, and counting in combinational and iterative networks. These include the application of traditional structures that rely on comparators/swappers as well as alternative networks with a variety of core elements such as adders, logical gates, and look-up tables. The iterative technique discussed in the book enables the sequential reuse of relatively large combinational blocks that execute many parallel operations with small propagation delays. For each type of network discussed, the main focus is on the step-by-step development of the architectures proposed from initial concepts to synthesizable hardware description language specifications. Each type of network is taken through several stages, including modeling the desired functionality in software, the retrieval and automatic conversion of key functions, leading to specifications for optimized hardware modules. The resulting specifications are then synthesized, implemented, and tested in FPGAs using commercial design environments and prototyping boards. The methods proposed can be used in a range of data processing applications, including traditional sorting, the extraction of maximum and minimum subsets from large data sets, communication-time data processing, finding frequently occurring items in a set, and Hamming weight/distance counters/comparators. The book is intended to be a valuable support material for university and industrial engineering courses that involve FPGA-based circuit and system design.

Fpga And Cell Processor Performance Optimization For Brain State In A Box Bsb Cognitive Computing

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Author :
language : en
Publisher:
Release Date : 2007

Fpga And Cell Processor Performance Optimization For Brain State In A Box Bsb Cognitive Computing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.

Emulation Of Bursting Neurons In Neuromorphic Hardware Based On Phase Change Materials

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Author : Richard Meyes
language : en
Publisher: diplom.de
Release Date : 2015-01-01

Emulation Of Bursting Neurons In Neuromorphic Hardware Based On Phase Change Materials written by Richard Meyes and has been published by diplom.de this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-01 with Computers categories.

In the history of computing hardware,Moore’s law, named after Intel co-founder Gordon E. Moore, describes a long-termtrend, whereby the number of transistors that can be placed inexpensively on an integrated circuit doubles approximately every two years [1]. Because the number of transistors is crucial for computing performance, significant performance gains could be achieved simply through complementary metal-oxide-semiconductor (CMOS) transistor downscaling. AlthoughMoore’s law, which was mentioned for the first time in 1965, turned out to persist for almost five decades, the nano era poses significant problems to the concept of downscaling [2]. Upon approaching the size of atoms, quantumeffects, such as quantum tunneling, pose fundamental barriers to the trend. Furthermore, the conventional computing paradigm based on the Von-Neumann architecture and binary logic becomes increasingly inefficient considering the growing complexity of todays computational tasks. Hence, new computational paradigms and alternative information processing architectures must be explored to extend the capabilities of future information technology beyond digital logic. A fantastic example for such an alternative information processing architecture is the human brain. The brain provides superior computational features such as ultrahigh density of processing units, low energy consumption per computational event, ultrahigh parallelism in computational execution, extremely flexible plasticity of connections between processing units and fault-tolerant computing provided by a huge number of computational entities. Compared to today’s programmable computers, biological systems are six to nine orders of magnitude more efficient in complex environments [3]. For instance: simulating five seconds of brain activity takes IBM’s state-of-the-art supercomputer Blue Gene a hundred times as long, i.e. 500 s, during which it consumes 1.4 MWof power, whereas the power dissipation in the human central nervous system is of the order of 10W[4, 5]. Thus, it is not only extremely interesting but in terms of computational progress also highly desirable to understand how information is processed in the human brain. The conceptual idea developed within the framework of this thesis tries to contribute to this intention. In contrast to most recent research dealing with the simulation and emulation of specific connections between nerve cells [5–12], the work of this thesis focuses on investigating, on [...]

Hardware Acceleration Of Eda Algorithms

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Author : Sunil P Khatri
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-03-11

Hardware Acceleration Of Eda Algorithms written by Sunil P Khatri and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-11 with Technology & Engineering categories.

Single-threaded software applications have ceased to see signi?cant gains in p- formance on a general-purpose CPU, even with further scaling in very large scale integration (VLSI) technology. This is a signi?cant problem for electronic design automation (EDA) applications, since the design complexity of VLSI integrated circuits (ICs) is continuously growing. In this research monograph, we evaluate custom ICs, ?eld-programmable gate arrays (FPGAs), and graphics processors as platforms for accelerating EDA algorithms, instead of the general-purpose sing- threaded CPU. We study applications which are used in key time-consuming steps of the VLSI design ?ow. Further, these applications also have different degrees of inherent parallelism in them. We study both control-dominated EDA applications and control plus data parallel EDA applications. We accelerate these applications on these different hardware platforms. We also present an automated approach for accelerating certain uniprocessor applications on a graphics processor. This monograph compares custom ICs, FPGAs, and graphics processing units (GPUs) as potential platforms to accelerate EDA algorithms. It also provides details of the programming model used for interfacing with the GPUs.

Fpga Implementation Acceleration Of Building Blocks For Biologically Inspired Computational Models

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Author : Mandar Deshpande
language : en
Publisher:
Release Date : 2011

Fpga Implementation Acceleration Of Building Blocks For Biologically Inspired Computational Models written by Mandar Deshpande and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computer input-output equipment categories.

In recent years there has been significant research in the field of computational neuroscience and many of these biologically inspired cognitive models are based on the theory of operation of mammalian visual cortex. One such model of neocortex developed by George & Hawkins, known as Hierarchical Temporal Memories (HTM), is considered for the research discussed here. We propose a simple hierarchical model that is derived from HTM. The aim of this work is to evaluate the hardware cost and performance against software based simulations. This work presents a detailed hardware implementation and analysis of the derived hierarchical model. We show that these networks are inherently parallel in their architecture, similar to the biological computing, and that parallelism can be exploited by massively parallel architectures implemented using reconfigurable devices such as the FPGA. Hardware implementation accelerates the learning process which is useful in many real world problems. We have implemented a complex network node that operates in real time using an FPGA. The current architecture is modular and allows us to estimate the hardware resources and computational units required to realize large scale networks in the future.

Fpga Based High Throughput Low Power Multi Core Neuromorphic Processor

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Author : Yangjie Qi
language : en
Publisher:
Release Date : 2015

Fpga Based High Throughput Low Power Multi Core Neuromorphic Processor written by Yangjie Qi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Adaptive routing (Computer network management) categories.

The interest in specialized neuromorphic computing architectures has been increasing recently, and several applications have been shown to be capable of being accelerated on such platforms. This thesis describes the implementation of multicore digital neuromorphic processing systems on FPGAs. Static and Dynamic routing were used to allow communication between the cores on the FPGA. Several applications were mapped to the system including image edge detection, MNIST image classification, and biometric ECG classification. Given that all the applications were implemented on the same processor (hence same base Verilog code), with only a change in the synaptic weights and number of neurons utilized, the system has the capability to accelerate a broad range of applications.

Fpga Based Hardware Acceleration Of Gravitational N Body Simulations

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Author : Loucas Louca
language : en
Publisher:
Release Date : 1997

Fpga Based Hardware Acceleration Of Gravitational N Body Simulations written by Loucas Louca and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.