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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. -
The Millennium Technology Prize Laureate 2010 Professor Stephen
1 (9) The Millennium Technology Prize Laureate 2010 “For his invention of the ARM microprocessor and its implementation on silicon chips. This invention has enabled the revolution in mobile electronics. To date, more than 18 billion ARM-based chips have been manufactured and are used in ubiquitous computing applications, such as mobile phones, digital photography and video, music players, fixed and wireless networking, automobiles and health care, benefitting a large number of people all over the world.” Professor Stephen Furber Professor of Computer Engineering, the University of Manchester, United Kingdom Born March 1953 in Manchester, United Kingdom. Timeline 1982 Acorn BBC Micro launched 1983 Acorn starts RISC Machine project with Furber as principal designer 1985 First ARM microprocessor produced 1987 ARM processor debuts as the first RISC processor for Acorn Archimedes desktop computer 1990 Advanced RISC Machines (ARM) spins out of Acorn and Apple Computers collaboration, Furber continues his research of low power computing as a professor at University of Manchester. 1998 ARM listed on the London Stock Exchange and NASDAQ. Over 50 million ARM powered products shipped. 2010 20 billion ARM based chips manufactured Creator of the ARM microprocessor The 2010 Millennium Technology Prize Laureate Steve Furber is the principal designer of the ARM 32- bit RISC microprocessor, an innovation that revolutionised mobile electronics. The ingeniously designed processor enabled the development of cheap, powerful handheld, battery-operated devices. In the past 25 years nearly 20 billion ARM based chips have been manufactured. You may never have heard of ARM microprocessors, but probably use at least one every day. They tick inside our mobile phones, mp3-players, video recorders and home routers. -
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. -
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. -
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]. -
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. -
1. Introduction Recently, a New Discipline Has Come up Which
JOURNAL OF REAL-TIME IMAGE PROCESSING SPECIAL ISSUE ON REAL-TIME NEUROMORPHIC SYSTEMS FOR IMAGE PROCESSING 1. Introduction Recently, a new discipline has come up which challenges classical approaches to computer vision research and engineering, the so-called neuromorphic engineering. This new research paradigm develops engineering systems with unparalleled robustness and on-line adaptability. Such systems are based on algorithms and techniques closely replicating and inspired from the principles of information processing in biological nervous systems. The robustness of the human visual system in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, and effortlessly. Their applicability to real-time computer vision challenges is extended, and includes prosthesis systems, robust human-computer interfaces, smart sensors, implanted electronic devices, autonomous visually-guided robotics systems. Neuromorphic engineering fills the gap between, on the one hand, computational neuroscience, and, on the other hand, traditional engineering. Computational neuroscience has yielded very useful models and theories of brain function, although often these have been restricted to simplified conditions, stimuli and tasks, to allow direct comparison with simple empirical measurements on biological systems. Also, computational neuroscience models typically are concerned with testing one given hypothesis, and thus are not intended to solve real-world problems, but they advance the understanding of brain function -
Embodied Neuromorphic Vision with Event-Driven Random Backpropagation
1 Embodied Neuromorphic Vision with Event-Driven Random Backpropagation Jacques Kaiser1, Alexander Friedrich1, J. Camilo Vasquez Tieck1, Daniel Reichard1, Arne Roennau1, Emre Neftci2, Rüdiger Dillmann1 Abstract—Spike-based communication between biological neu- rons is sparse and unreliable. This enables the brain to process visual information from the eyes efficiently. Taking inspiration from biology, artificial spiking neural networks coupled with silicon retinas attempt to model these computations. Recent findings in machine learning allowed the derivation of a family of powerful synaptic plasticity rules approximating backpropaga- tion for spiking networks. Are these rules capable of processing real-world visual sensory data? In this paper, we evaluate the performance of Event-Driven Random Back-Propagation (eRBP) at learning representations from event streams provided by a Dynamic Vision Sensor (DVS). First, we show that eRBP matches state-of-the-art performance on the DvsGesture dataset with the addition of a simple covert attention mechanism. By remapping visual receptive fields relatively to the center of the motion, this attention mechanism provides translation invariance at low computational cost compared to convolutions. Second, we successfully integrate eRBP in a real robotic setup, where a robotic arm grasps objects according to detected visual affordances. In this setup, visual information is actively sensed Figure 1: Our robotic setup embodying the synaptic learning by a DVS mounted on a robotic head performing microsaccadic rule eRBP [1]. The DVS is mounted on a robotic head per- eye movements. We show that our method classifies affordances forming microsaccadic eye movements. The spiking network within 100ms after microsaccade onset, which is comparable to is trained (dashed-line connections) in a supervised fashion human performance reported in behavioral study. -
B. Tech in Instrumentation & Control Engineering
Nirma University Institute of Technology Department of Instrumentation and Control Engineering B Tech in Instrumentation and Control Engineering 1 | P a g e MA101: Calculus [3 1 0 4] Learning outcomes: On completion of the course student CLO1: will be able to find higher ordered derivatives and hence represent function in power series of (x-a) CLO2: will apply the knowledge of function of several variables , its derivatives in engineering problems CLO3: will apply the knowledge of special functions(Gamma, Beta, Elliptic, Error) and its application in engineering problems CLO4: will apply the knowledge of multiple integration and its application in engineering problems Syllabus: Unit I: Differential Calculus Review of limits, continuity and differentiability, Successive differentiation, Leibnitz theorem (without proof), Indeterminate forms, Taylor’s and Maclaurin’s expansion of single variable, Partial Differentiation, Total derivative, Chain Rule, Implicit function, Euler’s theorem and its applications, Taylor’s and Maclaurin’s expansion of function of several variables, Maxima and Minima of function of several variables, Lagrange’s method of undetermined multipliers, Jacobian. Unit II: Integral Calculus Review of proper and improper integrals, Reduction formulae, Beta-Gamma functions, Error function, Tracing of curves, Rectification, Quadrature, Volume of solid of revolution, Area of surface of revolution, Double integral and evaluation, Change of order of integration, Change of variable, Triple integral and evaluation, Area using double integration, Volume as double and triple integration, Volume of solid by double integration. References: 1. Thomas’ Calculus(Latest edition), Pearson publication. 2. G B Thomas and R. L. Finney, Calculus and Analytic Geometry (Latest edition), Narosa Publication, Delhi. -
Review and Accounts 2012/2013 National Life Stories Chairman’S
Life NATIONAL stories Review and Accounts 2012/2013 National Life Stories Chairman’s When many people think about history, they think about initiated a series of innovative interviewing programmes books and documents, castles or stately homes. In fact history funded almost entirely from sponsorship, charitable and Foreword is all around us, in our own families and communities, in the individual donations and voluntary effort. living memories and experiences of older people. Everyone has a story to tell about their life which is unique to them. Each collection comprises recorded in-depth interviews of Whilst some people have been involved in momentous a high standard, plus content summaries and transcripts to historical events, regardless of age or importance we all assist users. Access is provided via the Sound and Moving have interesting life stories to share. Unfortunately, because Image Catalogue at www.cadensa.bl.uk and a growing memories die when people do, if we don’t record what number of interviews are made available for remote web people tell us, that history can be lost forever. use. Each individual life story interview is several hours long, covering family background, childhood, education, work, National Life Stories (NLS) was established in 1987 and its leisure and later life. mission is: ‘To record the first-hand experiences of as wide a cross section of society as possible, to preserve the recordings, Alongside the British Library’s other oral history holdings, to make them publicly available and encourage their use’. which stretch back to the beginning of the twentieth century, As an independent charitable trust within the Oral History NLS’s recordings form a unique and invaluable record of Section of the British Library, NLS’s key focus and expertise people’s lives in Britain today.