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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. -
Designing Sails Since 1969
SAILRITE SAIL KITS Designing sails since 1969. Catalina 30 Tall Rig Mainsail Kit by Frederick Leroy Carter F 31R Screecher Kit by Patrick Pettengill Capri 18 Main & Jib Sail Kits by Brent Stiles “ We built this sail ourselves!” Custom Lateen Main Kit by Steve Daigle -Karen Larson Building your own sail is a very rewarding and satisfying Each kit comes with the sail design data and a set of instructions experience. Not only is there a real sense of accomplishment, and illustrations that have been perfected from over 40 years of but the skills developed in the process will make you a more self- experience and feedback. Sail panels are pre-cut, labeled and reliant sailor. Sailrite makes the process very easy and affordable numbered for easy assembly. Panel overlap and hemming lines from start to finish by providing sail kits that include materials come plotted on each panel and double-sided tape is included used by professional sailmakers at up to 50% less the cost! to adhere panels together prior to sewing to ensure that draft and shape are maintained during construction. Batten pockets, Sailrite uses state-of-the-art design programs and hardware to windows, draft stripes, reef points, and other details will also prepare each kit. Sail panels and corner reinforcements are all come plotted on the appropriate panels if required for your sail. computer-cut and seaming lines are drawn along the edges. Draft, twist, and entry and exit curves are all carefully calculated, controlled, and positioned for each sail to maximize performance. Getting Started All materials are carefully selected by our sail designers to Getting started is easy and Sailrite’s expert staff is available toll best suit your application and only high quality sailcloths and free every working day to answer questions and help guide you laminates from Bainbridge, Challenge, Contender and others who through the ordering and construction process. -
Sailing Course Materials Overview
SAILING COURSE MATERIALS OVERVIEW INTRODUCTION The NCSC has an unusual ownership arrangement -- almost unique in the USA. You sail a boat jointly owned by all members of the club. The club thus has an interest in how you sail. We don't want you to crack up our boats. The club is also concerned about your safety. We have a good reputation as competent, safe sailors. We don't want you to spoil that record. Before we started this training course we had many incidents. Some examples: Ran aground in New Jersey. Stuck in the mud. Another grounding; broke the tiller. Two boats collided under the bridge. One demasted. Boats often stalled in foul current, and had to be towed in. Since we started the course the number of incidents has been significantly reduced. SAILING COURSE ARRANGEMENT This is only an elementary course in sailing. There is much to learn. We give you enough so that you can sail safely near New Castle. Sailing instruction is also provided during the sailing season on Saturdays and Sundays without appointment and in the week by appointment. This instruction is done by skippers who have agreed to be available at these times to instruct any unkeyed member who desires instruction. CHECK-OUT PROCEDURE When you "check-out" we give you a key to the sail house, and you are then free to sail at any time. No reservation is needed. But you must know how to sail before you get that key. We start with a written examination, open book, that you take at home. -
Further Devels'nent Ofthe Tunny
FURTHERDEVELS'NENT OF THETUNNY RIG E M H GIFFORDANO C PALNER Gi f ford and P art ners Carlton House Rlngwood Road Hoodl ands SouthamPton S04 2HT UK 360 1, lNTRODUCTION The idea of using a wing sail is not new, indeed the ancient junk rig is essentially a flat plate wing sail. The two essential characteristics are that the sail is stiffened so that ft does not flap in the wind and attached to the mast in an aerodynamically balanced way. These two features give several important advantages over so called 'soft sails' and have resulted in the junk rig being very successful on traditional craft. and modern short handed-cruising yachts. Unfortunately the standard junk rig is not every efficient in an aer odynamic sense, due to the presence of the mast beside the sai 1 and the flat shapewhich results from the numerousstiffening battens. The first of these problems can be overcomeby usi ng a double ski nned sail; effectively two junk sails, one on either side of the mast. This shields the mast from the airflow and improves efficiency, but it still leaves the problem of a flat sail. To obtain the maximumdrive from a sail it must be curved or cambered!, an effect which can produce over 5 more force than from a flat shape. Whilst the per'formanceadvantages of a cambered shape are obvious, the practical way of achieving it are far more elusive. One line of approach is to build the sail from ri gid componentswith articulated joints that allow the camberto be varied Ref 1!. -
East-1946.Pdf
YACHTING -THE u. s . ONE-DESIGN CLASS IDS ONE-DESIGN class, which is T sponsored by a group of yachtsmen The perm4nent b4ckst.,ywtll keep representing all three clubs at Marble head, bids fair to become one of our popu the rig in the bo"t while the run lar racmg classes. Developed on the boards ning b4ckst4y will be needed in p~liminary plans by Carl Alberg, of only to 4Ssure the jib st4nding Marblehead, who is as80ciated with the well or to t4ke the tug of the ~den office, the general dimensions of the '\ rspinn4ker~ !' "" new boat arc: length over all, 37' 9"; \ length on the water line, 24'; beam, 7'; draft, 5' 4"; displacement is 6450 pounds. \ Her sail area is 378 8quarc feet, of which 262 square feet is in the mainsail and 116 \ square feet in the jib. In addition, there is \ a genoa with an area of. 200 square feet and a parachute spinnaker. \ An interesting feature of the new boat is a light weight, portable cabin top · \ which is made in two sections and may be \ carried in bad weather or for overnight I cruising. The cockpit, with the cabin top · removed, runs all the way forward to the . \ mast to facilitate light sail handling with Fastenings will_be made of bronze, the out the necessity of going on deck. The keel will be of lead and her hollow spars helmsman is 80 placed that he will get no will be spruce. Fittings and rigging will be interference from his crew, yet he will be by Merriman Brothers. -
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. -
STANDARD SAIL and EQUIPMENT SPECIFICATIONS (Updated October, 2016)
STANDARD SAIL AND EQUIPMENT SPECIFICATIONS (Updated October, 2016) 1. Headsails, distinctions between jibs and spinnakers A. A headsail is defined as a sail in the fore triangle. It can be either a spinnaker, asymmetrical spinnaker or a jib. B. Distinction between spinnakers and jibs. A sail shall not be measured as a spinnaker unless the midgirth is 75% or more of the foot length and the sail is symmetrical about a line joining the head to the center of the foot. No jib may have a midgirth measured between the midpoints of luff and leech more than 50% of the foot length. Headsails with mid-girths, as cut, between 50% and 75% shall be handicapped on an individual basis. C. Asymmetrical spinnakers shall conform to the requirements of these specifications. 2. Definitions of jibs A. A jib is defined as any sail, other than a spinnaker that is to be set in the fore triangle. In any jib the midgirth, measured between the midpoints of the luff and leech shall not exceed 50% of the foot length nor shall the length of any intermediate girth exceed a value similarly proportionate to its distance from the head of the sail. B. A sailboat may use a luff groove device provided that such luff groove device is of constant section throughout its length and is either essentially circular in section or is free to rotate without restraint. C. Jibs may be sheeted from only one point on the sail except in the process of reefing. Thus quadrilateral or similar sails in which the sailcloth does not extend to the cringle at each corner are excluded.