From Digital Computing to Brain-Like Neuromorphic Accelerator
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Logic Circuits S
1 Normally-off logic based on resistive switches – Part II: Logic circuits S. Balatti, Student Member, IEEE, S. Ambrogio, Student Member, IEEE, D. Ielmini, Senior Member, IEEE Abstract—Logic gates based on the resistive switching memory (RRAM) allows for normally-off digital computing thanks to the nonvolatile nature of the RRAM switch [1]. An extremely small area consumption can be achieved thanks to the 2−terminal structure of the RRAM switch and its capability of 3D stacking. However, the RRAM organization details within the array must be thoroughly investigated. This work discusses the array orga- nization and the select/unselect schemes of RRAM logic circuits. We demonstrate a 1-bit adder to support the high functionality of RRAM logic. These results support RRAM as a promising technology for nonvolatile logic circuits beyond CMOS. Keywords: Resistive switching memory (RRAM), logic gates, logic circuits, logic computing Fig. 1. Schematic view of 2 stacked arrays with 1T1R architecture (a) and I. INTRODUCTION corresponding plan view (b). The selected columns are biased at V and 0, while the unselected at αV and γV. The selected row is floating and the Resistive switching memory (RRAM) is raising an in- unselected rows are biased at βV. Depending on its position in the array a creasing interest for high-density nonvolatile memories [2]– cell can be selected (S), unselected (U) or half selected (HA and HB). [5] and logic circuits [1], [6]–[8]. The crossbar latch was first proposed to allow inversion and restoration within diode high density, e.g., only 2F2 for an AND gate, which is more logic circuits [9]. -
Reverse Image Search for Scientific Data Within and Beyond the Visible
Journal, Vol. XXI, No. 1, 1-5, 2013 Additional note Reverse Image Search for Scientific Data within and beyond the Visible Spectrum Flavio H. D. Araujo1,2,3,4,a, Romuere R. V. Silva1,2,3,4,a, Fatima N. S. Medeiros3,Dilworth D. Parkinson2, Alexander Hexemer2, Claudia M. Carneiro5, Daniela M. Ushizima1,2,* Abstract The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released. Keywords Reverse image search — Content-based image retrieval — Scientific image recommendation — Convolutional neural network 1University of California, Berkeley, CA, USA 2Lawrence Berkeley National Laboratory, Berkeley, CA, USA 3Federal University of Ceara,´ Fortaleza, CE, Brazil 4Federal University of Piau´ı, Picos, PI, Brazil 5Federal University of Ouro Preto, Ouro Preto, MG, Brazil *Corresponding author: [email protected] aContributed equally to this work. -
Neural Network FAQ, Part 1 of 7
Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled. -
Unconventional Computing Using Memristive Nanodevices: from Digital Computing to Brain-Like Neuromorphic Accelerator
Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille. -
Neural Network FAQ, Part 1 of 7: Introduction
Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled. -
Neural Networks
SLAC-TN-03-028 Neural Networks Patrick Smith Office of Science, Student UndergraduateLaboratory Internship (SULI) Stanford University Stanford Linear Accelerator Center Menlo Park, California August 14,2003 Preparedin partial fulfillment of the requirementsof the Office of Science,Department of Energy’s ScienceUndergraduate Laboratory Internship under the direction of Tony Johnson. Participant: Signature ResearchAdvisor: Signature Work supported in part by the Department of Energy contract DE-AC03-76SF00515. INTRODUCTION Physicistsuse large detectorsto measureparticles createdin high-energy collisions at particle accelerators.These detectors typically producesignals indicating either where ionization occurs along the path of the particle, or where energyis depositedby the.particle.The data producedby thesesignals is fed into pattern recognition programsto try to identify what particles were produced,and to measurethe energy and direction of theseparticles. Ideally, there are many techniquesused in this pattern recognition software. One technique,neural networks, is particularly suitable for identifying what type of particle causedby a set of energy deposits. Neural networks can derive meaningfrom complicatedor imprecisedata, extract patterns,and detect trendsthat are too complex to be noticed by either humansor other computer related processes. To assistin the advancementof this technology,Physicists use a tool kit to experimentwith severalneural network techniques.The goal of this researchis interface a neural network tool kit into JavaAnalysis Studio (JAS3), an applicationthat allows data to be analyzedfrom any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desiredoutput while using JAS3 to analyzethe results or output. Before an implementationof a neural network can take place, a firm understandingof what a neural network is and how it works is beneficial. -
A Hybrid Nanomemristor/Transistor Logic Circuit Capable of Self-Programming
A hybrid nanomemristor/transistor logic circuit capable of self-programming Julien Borghetti, Zhiyong Li, Joseph Straznicky, Xuema Li, Douglas A. A. Ohlberg, Wei Wu, Duncan R. Stewart, and R. Stanley Williams1 Information and Quantum Systems Lab, Hewlett-Packard Laboratories, 1501 Page Mill Road, Palo Alto, CA 94304 Edited by Konstantin Likharev, State University of New York, Stony Brook University, and accepted by the Editorial Board December 19, 2008 (received for review July 9, 2008) Memristor crossbars were fabricated at 40 nm half-pitch, using Crossbars have been proposed for and implemented in a nanoimprint lithography on the same substrate with Si metal- variety of nanoscale electronic integrated circuit architectures, oxide-semiconductor field effect transistor (MOS FET) arrays to such as memory and logic systems (5–12). A 2-dimensional grid form fully integrated hybrid memory resistor (memristor)/transis- offers several advantages for computing at the nanoscale: It is tor circuits. The digitally configured memristor crossbars were used scalable down to the molecular scale (13, 14), it is a regular to perform logic functions, to serve as a routing fabric for inter- structure that can be configured by closing junctions to express connecting the FETs and as the target for storing information. As a high degree of complexity and reconfigured to tolerate defects an illustrative demonstration, the compound Boolean logic oper- in the circuit (15–17), and because of its structural simplicity it ation (A AND B) OR (C AND D) was performed with kilohertz can be fabricated inexpensively with nanoimprint lithography (7, frequency inputs, using resistor-based logic in a memristor crossbar 18, 19). -
European Journal of Computer Science and Information
European Journal of Computer Science and Information Technology Vol.9, No.2, pp.21-28, 2021 Print ISSN: 2054-0957 (Print), Online ISSN: 2054-0965 (Online) COMPARING THE TECHNOLOGIES USED FOR THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS Elda Xhumari PhD Candidate, University of Tirana, Faculty of Natural Sciences, Department of Informatics ABSTRACT: In the past few years, artificial neural networks (ANNs) have become a practice wherever it is necessary to solve problems of forecasting, classification, or control. ANNs are intuitively attractive because they are based on a primitive biological model of nervous systems. In the future, the development of such neurobiological models may lead to the creation of truly thinking computers. The areas of application of neural networks are very diverse - these are text and speech recognition, semantic search, expert systems, and decision support systems, prediction of stock prices, security systems, text analysis, etc. Based on the wide application of artificial neural networks, the application of numerous and diverse technologies can be inferred. In this research paper, a comparative approach towards the above-mentioned technologies will be undertaken in order to identify the optimal technology for each problem area in accordance with their respective advantages and disadvantages. KEYWORDS: artificial neural networks, applications, modules INTRODUCTION Artificial Neural Network (ANN) is a computational method that integrates the concepts of the human biological brain to computers for machine learning. The main purpose is to enable computers to adapt and learn the exact behaviour of human beings. As such, machine learning facilitates the continued processing of information that leads to the capacity to solve complex problems and equations that are beyond human ability (Goncalves et al., 2013). -
Data Mining Products
APPENDIX A Data Mining Products A.1 BIBLIOGRAPHIC NOTES The following paragraphs provide a superficial overview of data mining tools on the market. Links are provided to resources where more detailed information can be found. 4Thought • Product: 4Thought • Vendor: Cognos Inc. • URL: www.cognos.com/products/4thought • Functions: Prediction • Techniques: Neural networks (MLP) • Platforms: Windows 4Thought supports decision support applications, including forecasting. A spread- sheet interface is provided. Data can be accessed as text files, Excel, Lotus, dBase, Impromptu, Powerplay, and Scenario files. AC2 • Product: AC2 • Vendor: ISoft • URL: www.alice-soft.com/html/prod ac2.htm • Functions: Clustering, classification, prediction, segmentation • Techniques: Decision trees • Platforms: Unix, Windows AC2 is a complete data mining toolkit designed for knowledgeable users. AC2 has a graphical object-oriented modeling language and C/C++ libraries. Interactive editing of trees is supported. It behaves as a multiplatform library of data mining functions. Affinium Model • Product: Affinium Model • Vendor: Unica Corporation • URL: www.unicacorp.com • Functions: Classification, segmentation • Platforms: Unix, Windows Affinium Model provides CRM applications with segmentation capabilities. 274 Appendix A Data Mining Products 275 AI Trilogy • Product: AI Trilogy • Vendor: Ward Systems Group, Inc. • URL: www.wardsystems.com • Functions: Classification, forecasting, prediction • Techniques: Genetic algorithms, jackknife estimation, neural networks • Platforms: -
Neural Network Tools: an Overview from Work on "Automatic Classification of Alzheimer’S Disease from Structural MRI" Report for Master’S Thesis
Neural Network Tools: An Overview From work on "Automatic Classification of Alzheimer’s Disease from Structural MRI" Report for Master’s Thesis Eivind Arvesen Østfold University College [email protected] Abstract Machine learning has exploded in popularity during the last decade, and neural networks in particular have seen a resurgence due to the advent of deep learning. In this paper, we examine modern neural network software-libraries and describe the main differences between them, as well as their strengths and weaknesses. 1 Introduction at The University of Montreal, built with re- search in mind. It has a particular focus on The field of machine learning is now more al- deep learning. The library is focused on easy luring than ever, helping researchers and busi- configurability for expert users (i.e. machine nesses to tackle a variety of challenging prob- learning researchers) —unlike "black-box" li- lems such as development of autonomous vehi- braries, which provide good performance with- cles, visual recognition systems and computer- out demanding knowledge about underlying al- aided diagnosis. There is a plethora of different gorithms from users. One way to put it would tools and libraries available that aim to make be that the library values ease of experimenta- work on machine learning problems easier for tion over ease of use. users, and while most of the more general alter- natives are quite similar in terms of functional- Though Pylearn2 assumes some technical ity, they nonetheless differ in terms of approach sophistication on the part of users, the library and design goals. also has a focus on reusability, and the re- This report describes several popular ma- sulting modularity makes it possible to com- chine learning tools, attempts to showcase their bine and adapt several re-usable components strengths and characterizes situations in which to form working models, and to only learn they would be well suited for use. -
Performance of Neural Network Image Classification on Mobile CPU and GPU
Master’s Programme in Computer, Communication and Information Sciences Performance of Neural Network Image Classification on Mobile CPU and GPU Sipi Seppälä MASTER’S THESIS Aalto University MASTER’S THESIS 2018 Performance of Neural Network Image Classifcation on Mobile CPU and GPU Sipi Seppälä Otaniemi, 2018-04-20 Supervisor: Professor Antti Ylä-Jääski Advisors: M.Sc. Teemu Kämäräinen Dr. Matti Siekkinen Aalto University School of Science Master’s Programme in Computer, Communication and Information Sciences Abstract Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Sipi Seppälä Title Performance of Neural Network Image Classifcation on Mobile CPU and GPU School School of Science Master’s programme Computer, Communication and Information Sciences Major Computer Science Code SCI3042 Supervisor Professor Antti Ylä-Jääski Advisors M.Sc. Teemu Kämäräinen, Dr. Matti Siekkinen Level Master’s thesis Date 2018-04-20 Pages 86 Language English Abstract Artifcial neural networks are a powerful machine learning method, with impressive results lately in the feld of computer vision. In tasks like image classifcation, which is a well-known problem in computer vision, deep learning convolutional neural networks can even achieve human-level prediction accuracy. Although high-accuracy deep neural networks can be resource-intensive both to train and to deploy for inference, with the advent of lighter mobile-friendly neural network model architectures it is fnally possible to achieve real-time on-device inference without the need for cloud offoading. The inference performance can be further improved by utilizing mobile graphics processing units which are already capable of general-purpose parallel computing. This thesis measures and evaluates the performance aspects – execution latency, through- put, memory footprint, and energy usage – of neural network image classifcation inference on modern smartphone processors, namely CPU and GPU. -
Managing a Real-Time Massively-Parallel Neural Architecture
MANAGING A REAL-TIME MASSIVELY-PARALLEL NEURAL ARCHITECTURE A THESIS SUBMITTED TO THE UNIVERSITY OF MANCHESTER FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE FACULTY OF ENGINEERING AND PHYSICAL SCIENCES 2012 By James Cameron Patterson School of Computer Science Contents Abstract 11 Declaration 12 Copyright 13 Acknowledgements 14 The Author 15 1 Introduction 16 1.1 Research Motivation .......................... 17 1.1.1 Not Chips,but Cores with Everything ............. 17 1.1.2 Large System Management ................... 20 1.1.3 Neural Networks on Parallel Computers ............ 20 1.1.4 Monitoring the SpiNNaker Neural System ........... 21 1.2 Contributions .............................. 23 1.3 Publications ............................... 24 1.4 Thesis Overview ............................ 26 2 Neural Computing 29 2.1 Biology ................................. 30 2.2 Neural Network Modelling ....................... 33 2.2.1 First Generation Artificial Neural Networks (ANNs) ..... 34 2.2.2 Second Generation ANNs ................... 34 2.2.3 Third Generation ANNs .................... 36 2.3 Systems for Neural Network Modelling ................ 39 2.3.1 General Purpose Hardware ................... 40 2.3.2 Specialised Hardware Platforms ................ 45 2 2.3.3 The Centre Ground ....................... 48 3 The SpiNNaker Neural Architecture 50 3.1 Architectural Requirements ....................... 51 3.2 System Architecture ........................... 51 3.3 SpiNNaker Systems ........................... 53 3.4 SpiNNaker Communications .....................