FPGA Implementation of a PC-AT Computer to Neural Network Interface PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download FPGA Implementation of a PC-AT Computer to Neural Network Interface PDF full book. Access full book title FPGA Implementation of a PC-AT Computer to Neural Network Interface by Jeffrey Richard Lewis. Download full books in PDF and EPUB format.
Author: Jeffrey Richard Lewis
Publisher:
ISBN:
Category : Computer interfaces
Languages : en
Pages : 104
Get Book Here
Book Description
Author: Jeffrey Richard Lewis
Publisher:
ISBN:
Category : Computer interfaces
Languages : en
Pages : 104
Get Book Here
Book Description
Author: Amos R. Omondi
Publisher: Springer Science & Business Media
ISBN: 0387284877
Category : Technology & Engineering
Languages : en
Pages : 365
Get Book Here
Book Description
During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.
Author: Piotr Antonik
Publisher: Springer
ISBN: 3319910531
Category : Science
Languages : en
Pages : 187
Get Book Here
Book Description
This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.
Author: Li Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
Get Book Here
Book Description
This thesis proposes to implement a new parallel convolutional binarized neural network (i.e. PC-BNN) on FPGA with accurate inference. The embedded PC-BNN is designed for image classification on CIFAR-10 dataset and explores the hardware architecture and optimization of customized CNN topology.
Author: Avvaru Srinivasulu
Publisher: LAP Lambert Academic Publishing
ISBN: 9783848435456
Category : Field programmable gate arrays
Languages : en
Pages : 76
Get Book Here
Book Description
This work was to establish whether it was possible to achieve a reasonable speedup by implementing FPGA based Hopfield neural networks for some simple constraint satisfaction problems. The results are significant - our initial implementation using standard Xilinx FPGAs yielded 2-3 orders of magnitude speedup over the Sun Blade 2000 workstation comes with 1.2-GHz version of the 64-bit UltraSPARC III Cu processor. The main problem with the work to date is that the problems are both unrealistically small and simplistic. That is the constraints on the N-Queen problem are simpler than those found in many real world scheduling applications. Thus, it is not clear whether we will be able to optimize the neuron structure for more complex problems since the weights matrix may not contain as many zero elements. Thus a new method for speed improvement of Hopfield neural networks for solving constraint satisfaction problems using Field Programmable Gate Arrays (FPGAs) was proposed and implemented.
Author:
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 59
Get Book Here
Book Description
Brain-computer interface (BCI) has been extensively studied as a means of restoring sensorimotor functions. The BCI enables individuals to use the electrical activity signals of their own brain that are recorded by electrodes, to control an external device through decoding, translating, and actuating. Most state-of-the-art decoding techniques rely on offline analysis, making it impractical for portable BCI to implement complex computation on hardware. On the other hand, classification capability based on look-up table is limited in onchip implementation. An on-chip intelligent system based on Artificial Neural Network (ANN) has been designed that can effectively perform ECoG data signal decoding of a single finger movement. The main building blocks of this decoding architecture are a hardware friendly version of principle component analysis (PCA) and a multi-layer perceptron (MLP). In this thesis, we mainly focus on the hardware implementation of multi-layer perceptron that can perform movement classification. Training of the neural network is carried out and learned weights are used to model the ANN in Verilog hardware description language and made it FPGA implementable. Various architectures of ANN were considered to optimize the design in terms of performance trade-offs such as area, power, speed and accuracy. Our proposed architecture can predict single finger movements with more than 80% accuracy. This implementation will serve as a pathway to develop a real-time BCI system capable of predicting volitional movement intentions.
Author: Omar Sadeq Salman
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 104
Get Book Here
Book Description
This project may be seen as a place to begin for learning Artificial Neural Network (ANN).
Author: Muhammad Mohid Nabil
Publisher:
ISBN:
Category : Convolutions (Mathematics)
Languages : en
Pages :
Get Book Here
Book Description
With the improvement in processing systems, machine learning applications are finding widespread use in almost all sectors of technology. Image recognition is one application of machine learning which has become widely popular with various architectures and systems aimed at improving recognition performance. With classification accuracy now approaching saturation point, many researchers are now focusing on resource and energy efficiency. With the increased demand for learning applications in embedded devices, it is of paramount importance to optimize power and energy consumption to increase utility in these low power embedded systems. In recent months, reduced precision neural networks have caught the attention of some researchers. Reduced data width deep nets offer the potential of saving valuable resources on hardware platforms. In turn, these hardware platforms such as Field Programmable Gate Arrays (FPGAs) offer the potential of a low power system with massive parallelism increasing throughput and performance. In this research, we explore the implementations of a deep learning architecture on FPGA in the presence of resource and energy constraints. We study reduced precision neural networks and implement one such architecture as a proof of concept. We focus on binarized convolutional neural network and its implementation on FPGAs. Binarized convolutional nets have displayed a classification accuracy of up to 88% with some smaller image sets such as CIFAR-10. This number is on the rise with some of the new architectures. We study the tradeoff between architecture depth and its impact on accuracy to get a better understanding of the convolutional layers and their impact on the overall performance. This is done from a hardware perspective giving us better insight enabling better resource allocation on FPGA fabric. Zynq ZCU-102 has been used for accelerator implementation. High level synthesis tool (Vivado HLS) from Xilinx is used for CNN definition on FPGA fabric.
Author: Hamzah Asyrani Sulaiman
Publisher: Springer
ISBN: 3319245848
Category : Technology & Engineering
Languages : en
Pages : 1282
Get Book Here
Book Description
This book covers diverse aspects of advanced computer and communication engineering, focusing specifically on industrial and manufacturing theory and applications of electronics, communications, computing and information technology. Experts in research, industry, and academia present the latest developments in technology, describe applications involving cutting-edge communication and computer systems, and explore likely future trends. In addition, a wealth of new algorithms that assist in solving computer and communication engineering problems are presented. The book is based on presentations given at ICOCOE 2015, the 2nd International Conference on Communication and Computer Engineering. It will appeal to a wide range of professionals in the field, including telecommunication engineers, computer engineers and scientists, researchers, academics and students.
Author: Akshay Raju Krishnani
Publisher:
ISBN:
Category : Field programmable gate arrays
Languages : en
Pages :
Get Book Here
Book Description
The main objective of this thesis is to investigate the efficiency of in-situ trainable Convolutional Neural Networks (CNNs) on modern programmable System-on-Chip (SoC) Field Programmable Gate Arrays (FPGAs) composed of embedded processors and reconfigurable fabric and to study the robustness of the system when faults happen. One particular characteristic of this work is that CNN is developed exclusively using High-Level Synthesis (HLS), particularly in SystemC, generating Verilog code. In this thesis, the feature maps are also being trained on the FPGA, which is traditionally done offline. The CNN architecture is instantiated on the FPGA and weights are trained through the software model on the ARM processor embedded into the FPGA and updated in the architecture through the AXI bus interface. Moreover, since CNN is implemented in hardware the resource used need to be minimized. This allows to choose a smaller, and cheaper FPGA, as well as reducing the total power consumption. To address this, the effect of bitwidth reduction of the CNN is investigated with respect to the accuracy of handwritten characters recognitions. Finally, the robustness of the CNN is analyzed by breaking internal connection of different neurons studying how the accuracy drops when the fault happens at different layers If the accuracy is reduced, then the CNN is re-trained in-situ to increase the accuracy of the CNN.
