Neuromorphic Vision: Sensors to Event-Based Algorithms Q2 7
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A Morphic Based Live Programming Graphical User Interface Implemented in Python
Final Thesis PyMorphic - a Morphic based Live Programming Graphical User Interface implemented in Python by Anders Osterholm¨ LIU-KOGVET-D--06/12--SE 23th August 2006 PyMorphic - a Morphic based Live Programming Graphical User Interface implemented in Python Final Thesis performed at Human Centered Systems division in the Department of Computer and Information Science at Link¨oping University by Anders Osterholm¨ LIU-KOGVET-D--06/12--SE 23th August 2006 Examiner: Dr. Mikael Kindborg Department of Computer and Information Science at Link¨oping University Abstract Programming is a very complex activity that has many simultaneous learn- ing elements. The area of Live-programming offers possibilities for enhanc- ing programming work by speeding up the feedback loop and providing means for reducing the cognitive load on the working memory during the task. This could allow for better education for novice programmers. In this work a number of systems with a shared aim of providing educational tools for scholars from compulsory level to undergraduate college were studied. The common approach in the majority of the tools was to use program ab- stractions like tangible morphs, playing cards, capsules for code segments, and visual stories. For the user these abstractions and tools offer better focus on the constructive and creative side of programming because they relieve the user from the cumbersome work of writing program code, but they also sacrifice some of the expressiveness of a low-level language. A Live programming system, called PyMorphic, based on the Morphic model was built in the Python programming language. Two different so- lutions, based on the Wx toolkit for Python, were constructed and evalu- ated. -
Neural Network Transformation and Co-Design Under Neuromorphic Hardware Constraints
NEUTRAMS: Neural Network Transformation and Co-design under Neuromorphic Hardware Constraints Yu Ji ∗, YouHui Zhang∗‡, ShuangChen Li†,PingChi†, CiHang Jiang∗,PengQu∗,YuanXie†, WenGuang Chen∗‡ Email: [email protected], [email protected] ∗Department of Computer Science and Technology, Tsinghua University, PR.China † Department of Electrical and Computer Engineering, University of California at Santa Barbara, USA ‡ Center for Brain-Inspired Computing Research, Tsinghua University, PR.China Abstract—With the recent reincarnations of neuromorphic (CMP)1 with a Network-on-Chip (NoC) has emerged as a computing comes the promise of a new computing paradigm, promising platform for neural network simulation, motivating with a focus on the design and fabrication of neuromorphic chips. many recent studies of neuromorphic chips [4]–[10]. The A key challenge in design, however, is that programming such chips is difficult. This paper proposes a systematic methodology design of neuromorphic chips is promising to enable a new with a set of tools to address this challenge. The proposed computing paradigm. toolset is called NEUTRAMS (Neural network Transformation, One major issue is that neuromorphic hardware usually Mapping and Simulation), and includes three key components: places constraints on NN models (such as the maximum fan- a neural network (NN) transformation algorithm, a configurable in/fan-out of a single neuron, the limited range of synaptic clock-driven simulator of neuromorphic chips and an optimized runtime tool that maps NNs onto the target hardware for weights) that it can support, and the hardware types of neurons better resource utilization. To address the challenges of hardware or activation functions are usually simpler than the software constraints on implementing NN models (such as the maximum counterparts. -
Feihong Hsu 217 S
Feihong Hsu 217 S. Leavitt St., #1S Chicago, IL 60612 Cell: 847.219.6000, Home: 312.738.3179 [email protected] www.cs.uic.edu/~fhsu EDUCATION: University of Illinois at Chicago Expected graduation in May 2004 Master of Science in Computer Science GPA: 4.80/5.00 University of Illinois at Urbana-Champaign Graduated December 2000 Bachelor of Science in Mathematics & Computer Science GPA: 3.78/4.00 Courses : Software Engineering, Database Systems, User Interface Design, Computer Networks, Object-Oriented Languages & Environments, Computer Architecture, Numerical Methods, Combinatorial Algorithms, Programming Languages & Compilers, Artificial Intelligence EXPERIENCE: Teaching Assistant , Introduction to Programming , August 2002-December 2003 University of Illinois at Chicago, Computer Science Department http://logos.cs.uic.edu/102 • Teach up to 30 students in weekly lab sections (C/C++/Java). • Develop online material, quizzes, lab assignments. Assist in writing exam problems. • Proposed and implemented new hands-on approach to labs, causing students to become visibly more engaged in the material. • Designed assignment in which students programmed an image manipulation application which included convolution and distortion filters. Teaching Assistant , Computer Literacy , January 2004-Present University of Illinois at Chicago, Computer Science Department http://wiggins.cs.uic.edu/cs100 • Most duties are similar to above. • Propose course topics and determine direction of the course Research Assistant , Team Engineering Collaboratory (TEC) , May 2000-December 2001 University of Illinois at Urbana-Champaign, Speech Communications Department http://www.spcomm.uiuc.edu/Projects/TECLAB/ • Continued development of Blanche, a modeling and simulation environment for the study of social networks. • Managed two undergrad programmers. Maintained project web page and distributions. -
Deep Learning with Tensorflow 2 and Keras Second Edition
Deep Learning with TensorFlow 2 and Keras Second Edition Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API Antonio Gulli Amita Kapoor Sujit Pal BIRMINGHAM - MUMBAI Deep Learning with TensorFlow 2 and Keras Second Edition Copyright © 2019 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Commissioning Editor: Amey Varangaonkar Acquisition Editors: Yogesh Deokar, Ben Renow-Clarke Acquisition Editor – Peer Reviews: Suresh Jain Content Development Editor: Ian Hough Technical Editor: Gaurav Gavas Project Editor: Janice Gonsalves Proofreader: Safs Editing Indexer: Rekha Nair Presentation Designer: Sandip Tadge First published: April 2017 Second edition: December 2019 Production reference: 2130320 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-83882-341-2 www.packt.com packt.com Subscribe to our online digital library for full access to over 7,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. -
Nordic ID Morphic User Manual
Nordic ID Morphic User Manual Nordic ID International Headquarters Phone: +358 2 727 7700 Myllyojankatu 2A Fax: +358 2 727 7720 FI-24100 SALO Email: [email protected] FINLAND www.nordicid.com Table of Contents 3.3 Stylus and Touch Screen............................ 20 3.3.1 Input Panel: Soft Keyboard ................ 21 (i) A few words of caution..................................... 4 3.4 Rebooting your Nordic ID Morphic ............ 21 3.4.1 Soft-resetting your device................... 21 (ii) Trademarks......................................................... 4 3.4.2 Hard-resetting your device ................. 22 (iii) Safety precautions .......................................... 5 3.5 Registry ......................................................... 22 Laser beam ........................................................... 5 3.6 Non-volatile Storage.................................... 23 Note of caution about the laser beam in 3.7 Control Panel................................................ 23 European languages............................................ 6 3.7.1 Accessing Control Panel..................... 24 3.7.2 Opening Applet .................................... 24 Battery.................................................................... 7 3.7.3 Closing Applet ...................................... 24 Wall charger.......................................................... 8 3.7.4 Closing Control Panel ......................... 24 Medical devices.................................................... 8 3.8 Control Panel Applets -
A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons
ORIGINAL RESEARCH published: 28 July 2021 doi: 10.3389/fnins.2021.715451 A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons Ahana Gangopadhyay and Shantanu Chakrabartty* Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, United States Growth-transform (GT) neurons and their population models allow for independent control over the spiking statistics and the transient population dynamics while optimizing a physically plausible distributed energy functional involving continuous-valued neural variables. In this paper we describe a backpropagation-less learning approach to train a Edited by: network of spiking GT neurons by enforcing sparsity constraints on the overall network Siddharth Joshi, spiking activity. The key features of the model and the proposed learning framework University of Notre Dame, United States are: (a) spike responses are generated as a result of constraint violation and hence Reviewed by: can be viewed as Lagrangian parameters; (b) the optimal parameters for a given task Wenrui Zhang, can be learned using neurally relevant local learning rules and in an online manner; University of California, Santa Barbara, United States (c) the network optimizes itself to encode the solution with as few spikes as possible Youhui Zhang, (sparsity); (d) the network optimizes itself to operate at a solution with the maximum Tsinghua University, China dynamic range and away from saturation; and (e) the framework is flexible enough to *Correspondence: incorporate additional structural and connectivity constraints on the network. As a result, Shantanu Chakrabartty [email protected] the proposed formulation is attractive for designing neuromorphic tinyML systems that are constrained in energy, resources, and network structure. -
Multilayer Spiking Neural Network for Audio Samples Classification Using
Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker Juan Pedro Dominguez-Morales, Angel Jimenez-Fernandez, Antonio Rios-Navarro, Elena Cerezuela-Escudero, Daniel Gutierrez-Galan, Manuel J. Dominguez-Morales, and Gabriel Jimenez-Moreno Robotic and Technology of Computers Lab, Department of Architecture and Technology of Computers, University of Seville, Seville, Spain {jpdominguez,ajimenez,arios,ecerezuela, dgutierrez,mdominguez,gaji}@atc.us.es http://www.atc.us.es Abstract. Audio classification has always been an interesting subject of research inside the neuromorphic engineering field. Tools like Nengo or Brian, and hard‐ ware platforms like the SpiNNaker board are rapidly increasing in popularity in the neuromorphic community due to the ease of modelling spiking neural networks with them. In this manuscript a multilayer spiking neural network for audio samples classification using SpiNNaker is presented. The network consists of different leaky integrate-and-fire neuron layers. The connections between them are trained using novel firing rate based algorithms and tested using sets of pure tones with frequencies that range from 130.813 to 1396.91 Hz. The hit rate percentage values are obtained after adding a random noise signal to the original pure tone signal. The results show very good classification results (above 85 % hit rate) for each class when the Signal-to-noise ratio is above 3 decibels, vali‐ dating the robustness of the network configuration and the training step. Keywords: SpiNNaker · Spiking neural network · Audio samples classification · Spikes · Neuromorphic auditory sensor · Address-Event Representation 1 Introduction Neuromorphic engineering is a discipline that studies, designs and implements hardware and software with the aim of mimicking the way in which nervous systems work, focusing its main inspiration on how the brain solves complex problems easily. -
Pharo Morphic Application Framework
Pharo Morphic Application Framework [email protected] Abstract The purpose of this framework is to have a comprehensive Pharo Application hierarchy to easily construct an enterprise ready application, that is scalable and built out as modules that link together. Morphic View Framework: Simplify creating Morphic UI with creating views from Components/ Parts / Modular UI construction: Create multiple pieces of independent views and compose it into one composite application view easily with: • Specs based View Construction • Simple Morphic View • Panels Composition: Complex GUI • API for: View attributes initializations intervention in intermediate steps • SupervisoryController aka MVP Presenter • Locale based translation integrated • LoggerManager to easily plugin Toothpick logger • Configuration based constants from external properties file / defaults code based.. • Named Prototype to optimize space/ time complexities as well as extension to dependency injection • Keyboard Event Handler at view level • Mouse Listener at a view level • Flexible Explicit View Construction Additionally incorporate existing/create standard frameworks, possibly as plugins or independent package, for: • Evolve a UI Builder that is a simple drag n drop capability of widgets • Complete RPC capabilites over XMLRPC for various purposes vis: • a JDBC connector along with an example complimentary Groovy Server backend / Groovy Client code References: 1. Various Smalltalk implementations: MVC architecture leading to the SupervisoryController architecture of MVP. 2. Spring Framework 3. General Enterprise Application Architecture patterns Motivation: Smalltalk is a beautiful and enjoyable language. Morphic is a wonderful but most under exploited UI framework that has immense potential to exponentially grow with contribution from the world of developers. Enterprise developers today are not experts, but casual programmers who depend on existing frameworks, patterns to adapt quickly. -
Neuromorphic Computing Using Emerging Synaptic Devices: a Retrospective Summary and an Outlook
electronics Review Neuromorphic Computing Using Emerging Synaptic Devices: A Retrospective Summary and an Outlook Jaeyoung Park 1,2 1 School of Computer Science and Electronic Engineering, Handong Global University, Pohang 37554, Korea; [email protected]; Tel.: +82-54-260-1394 2 School of Electronic Engineering, Soongsil University, Seoul 06978, Korea Received: 29 June 2020; Accepted: 5 August 2020; Published: 1 September 2020 Abstract: In this paper, emerging memory devices are investigated for a promising synaptic device of neuromorphic computing. Because the neuromorphic computing hardware requires high memory density, fast speed, and low power as well as a unique characteristic that simulates the function of learning by imitating the process of the human brain, memristor devices are considered as a promising candidate because of their desirable characteristic. Among them, Phase-change RAM (PRAM) Resistive RAM (ReRAM), Magnetic RAM (MRAM), and Atomic Switch Network (ASN) are selected to review. Even if the memristor devices show such characteristics, the inherent error by their physical properties needs to be resolved. This paper suggests adopting an approximate computing approach to deal with the error without degrading the advantages of emerging memory devices. Keywords: neuromorphic computing; memristor; PRAM; ReRAM; MRAM; ASN; synaptic device 1. Introduction Artificial Intelligence (AI), called machine intelligence, has been researched over the decades, and the AI now becomes an essential part of the industry [1–3]. Artificial intelligence means machines that imitate the cognitive functions of humans such as learning and problem solving [1,3]. Similarly, neuromorphic computing has been researched that imitates the process of the human brain compute and store [4–6]. -
Static and Runtime API Usage Analysis on .NET Diplomarbeit
Fachbereich 4: Informatik Static and Runtime API Usage Analysis on .NET Diplomarbeit zur Erlangung des Grades eines Diplom-Informatikers im Studiengang Informatik vorgelegt von Rufus Linke Erstgutachter: Prof. Dr. Ralf Lämmel Institut für Informatik, AG Softwaresprachen Zweitgutachter: Dipl. Math. Ekaterina Pek Institut für Informatik, AG Softwaresprachen Koblenz, im März 2011 IchERKLÄRUNG versichere, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel benutzt habe. Ja Nein Mit der Einstellung der Arbeit in die Bibliothek bin ich einver- standen. Der Veröffentlichung dieser Arbeit im Internet stimme ich zu. ........................................................................................ (Ort, Datum) (Unterschrift) TheABSTRACT reuse of existing software libraries and frameworks, commonly referenced by their API, has generally been established in modern software development. But as software evolves, so do APIs and thus various reasons for an existing project to migrate from one API to another can occur. To support research on such API migration, this work gathers fundamental API usage data from real world projects. Such data can be helpful to identify the most significant aspects of an API and those that need to be treated cautiously when performing transformations. The present work uses a corpus of 18 manually selected and mature open-source soft- ware projects for this purpose. The analysis methods are driven by both static and dy- namic approaches. A bytecode-based process statically analyzes the structure of a soft- ware to discover its external API feature usages. Then, a dynamic analysis takes advan- tage of instrumentation to collect data about the execution of the static feature references as well as additional information that is only available at runtime like the type of the receiver object of a virtual method call. -
Neuromorphic Processing and Sensing
1 Neuromorphic Processing & Sensing: Evolutionary Progression of AI to Spiking Philippe Reiter, Geet Rose Jose, Spyridon Bizmpikis, Ionela-Ancuța Cîrjilă machine learning and deep learning (MLDL) developments. Abstract— The increasing rise in machine learning and deep This accelerated pace of innovation was spurred on by the learning applications is requiring ever more computational seminal works of LeCun, Hinton and others in the 1990s on resources to successfully meet the growing demands of an always- convolutional neural networks, or CNNs. Since then, numerous, connected, automated world. Neuromorphic technologies based on more advanced learning models have been developed, and Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the neural networks have become integral to industry, medicine, computations and power requirements by modeling the academia and commercial devices. From fully autonomous functioning, and spiking, of the human brain. With the vehicles to rapid facial recognition entering popular culture to proliferation of tools and platforms aiding data scientists and enumerable innovations across almost all domains, it is not an machine learning engineers to develop the latest innovations in exaggeration to claim that CNNs and their progeny have artificial and deep neural networks, a transition to a new paradigm will require building from the current well-established changed both the technological and physical worlds. foundations. This paper explains the theoretical workings of CNNs can be exceedingly computationally intensive, neuromorphic technologies based on spikes, and overviews the however, as will be explained, often limiting their use to high- state-of-art in hardware processors, software platforms and performance computers and large data centres. -
Neuromorphic Computing in Ginzburg-Landau Lattice Systems
Neuromorphic computing in Ginzburg-Landau polariton lattice systems Andrzej Opala and Michał Matuszewski Institute of Physics, Polish Academy of Sciences, Warsaw Sanjib Ghosh and Timothy C. H. Liew Division of Physics and Applied Physics, Nanyang Technological University, Singapore < Andrzej Opala and Michał Matuszewski Institute of Physics, Polish Academy of Sciences, Warsaw Sanjib Ghosh and Timothy C. H. Liew Division of Physics and Applied Physics, Nanyang Technological University, Singapore < Machine learning with neural networks What is machine learning? Modern computers are extremely efficient in solving many problems, but some tasks are very hard to implement. ● It is difficult to write an algorithm that would recognize an object from different viewpoints or in di erent sceneries, lighting conditions etc. ● It is difficult to detect a fradulent credit card transaction. !e need to combine a large number of weak rules with complex dependencies rather than follow simple and reliable rules. "hese tasks are often relatively easy for humans. Machine learning with neural networks Machine learning algorithm is not written to solve a speci#c task, but to learn how to classify / detect / predict on its own. ● A large number of examples is collected and used as an input for the algorithm during the teaching phase. ● "he algorithm produces a program &eg. a neural network with #ne tuned weights), which is able to do the job not only on the provided samples, but also on data that it has never seen before. It usually contains a great number of parameters. Machine learning with neural networks "hree main types of machine learning: 1.Supervised learning – direct feedback, input data is provided with clearly de#ned labels.