Fpga Implementation Acceleration Of Building Blocks For Biologically Inspired Computational Models
DOWNLOAD
Download Fpga Implementation Acceleration Of Building Blocks For Biologically Inspired Computational Models PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Fpga Implementation Acceleration Of Building Blocks For Biologically Inspired Computational Models book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Fpga Implementation Acceleration Of Building Blocks For Biologically Inspired Computational Models
DOWNLOAD
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.
Approximate Arithmetic Circuit Architectures For Fpga Based Systems
DOWNLOAD
Author : Salim Ullah
language : en
Publisher: Springer Nature
Release Date : 2023-02-27
Approximate Arithmetic Circuit Architectures For Fpga Based Systems written by Salim Ullah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-27 with Technology & Engineering categories.
This book presents various novel architectures for FPGA-optimized accurate and approximate operators, their detailed accuracy and performance analysis, various techniques to model the behavior of approximate operators, and thorough application-level analysis to evaluate the impact of approximations on the final output quality and performance metrics. As multiplication is one of the most commonly used and computationally expensive operations in various error-resilient applications such as digital signal and image processing and machine learning algorithms, this book particularly focuses on this operation. The book starts by elaborating on the various sources of error resilience and opportunities available for approximations on various layers of the computation stack. It then provides a detailed description of the state-of-the-art approximate computing-related works and highlights their limitations.
Neuroscience Computing Performance And Benchmarks Why It Matters To Neuroscience How Fast We Can Compute
DOWNLOAD
Author : Felix Schürmann
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-26
Neuroscience Computing Performance And Benchmarks Why It Matters To Neuroscience How Fast We Can Compute written by Felix Schürmann and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-26 with Science categories.
Fpga Based Hardware Acceleration For Brain State In A Box Models In Neoromorphic Computing
DOWNLOAD
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.
Symbiosis Of Program Analysis And Biologically Inspired Computational Models
DOWNLOAD
Author : Marko Vasic
language : en
Publisher:
Release Date : 2022
Symbiosis Of Program Analysis And Biologically Inspired Computational Models written by Marko Vasic and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Recent years witnessed increased utility of biologically inspired computational models. Neural networks transformed the industry by enabling a plethora of applications in computer vision, and natural language processing, among others. Chemical reaction networks started gaining increased interest within the area of molecular programming considering the wide range of potential applications in synthetic biology, medicine, nanofabrication and other fields. In order to program desired behavior in a manner similar to conventional programming, both of the models require intuitive and effective programming paradigms. However, programs are susceptible to errors, and are hard to systematically analyze and synthesize. My thesis is that there exist natural symbiotic connections between the three currently known models of computation: conventional programming, neural networks, and chemical computation. This insight provides a sound basis for developing novel techniques for effective and efficient analysis and synthesis of the computation. My dissertation makes four key contributions. First, it presents the first imperative programming language that translates to CRNs. This enables an intuitive way to specify computation in chemistry. Second, it presents a program synthesis technique for discovering small CRNs exhibiting a desired functionality. This enables discovery of basic primitives (instructions) that can help inform a design of efficient high level programming paradigms for chemistry. Third, it presents a tight connection between a subclass of neural networks and CRNs. This enables programming chemistry with data using well founded deep learning techniques, thus opening the door for novel applications that are prohibitively hard to be encoded via imperative logic. Fourth, it presents a technique using imperative programs and formal logic to mimic neural networks and verify their properties, and conversely uses neural networks to help fix errors in imperative programs