Portable Test and Stimulus Standard
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Neuron C Reference Guide Iii • Introduction to the LONWORKS Platform (078-0391-01A)
Neuron C Provides reference info for writing programs using the Reference Guide Neuron C programming language. 078-0140-01G Echelon, LONWORKS, LONMARK, NodeBuilder, LonTalk, Neuron, 3120, 3150, ShortStack, LonMaker, and the Echelon logo are trademarks of Echelon Corporation that may be registered in the United States and other countries. Other brand and product names are trademarks or registered trademarks of their respective holders. Neuron Chips and other OEM Products were not designed for use in equipment or systems, which involve danger to human health or safety, or a risk of property damage and Echelon assumes no responsibility or liability for use of the Neuron Chips in such applications. Parts manufactured by vendors other than Echelon and referenced in this document have been described for illustrative purposes only, and may not have been tested by Echelon. It is the responsibility of the customer to determine the suitability of these parts for each application. ECHELON MAKES AND YOU RECEIVE NO WARRANTIES OR CONDITIONS, EXPRESS, IMPLIED, STATUTORY OR IN ANY COMMUNICATION WITH YOU, AND ECHELON SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of Echelon Corporation. Printed in the United States of America. Copyright © 2006, 2014 Echelon Corporation. Echelon Corporation www.echelon.com Welcome This manual describes the Neuron® C Version 2.3 programming language. It is a companion piece to the Neuron C Programmer's Guide. -
Portable Stimulus: What's Coming in 1.1
Portable Stimulus: What’s Coming in 1.1 and What it Means For You Portable Stimulus Working Group PSS 1.1 Tutorial Agenda • What is PSS Introduction • Tom Fitzpatrick, • Abstract DMA model in PSS 1.0 Mentor, a Siemens Business Memory • The problem • Prabhat Gupta, AMD Allocation • New PSS concepts Higher-Level • The problem • Matan Vax, Scenarios • New constructs Cadence Design Systems • The problem • Karthick Gururaj, HSI Realization • New concepts and constructs Vayavya Labs System-Level • Portability • Hillel Miller, Synopsys Usage • Complex scenarios • Summary Special Thanks to: Conclusion • What’s next Dave Kelf, Breker Verification Systems Josh Rensch, Semifore 2 The Need for Verification Abstraction Test content authoring represents major proportion of development SIMULATION Disconnected cross-process methods Test Content • Block EMULATION • UVM tests laborious, error-prone • SoC FPGA PROTO • Hard to hit corner-cases with C tests • Post-Silicon • Disconnected diagnostic creation IP BLOCK SUBSYSTEM FULL SYSTEM Test portability, reuse, scaling, maintenance all problematic 3 Key Aspects of Portable Stimulus Capture pure Partial scenario Composable Formal Automated test Target multiple test intent description scenarios representation generation platforms of test space Separate test intent from implementation High-coverage test generation across the verification process with much less effort 4 PSS Improves Individual Verification Phases IP BLOCK SUB-SYSTEM FULL SYSTEM Create block-level (UVM) tests & Easily model system-level Generate -
Arm Cortex-M System Design Kit Technical Reference Manual
Arm® Cortex®-M System Design Kit Revision: r1p1 Technical Reference Manual Copyright © 2011, 2013, 2017 Arm Limited (or its affiliates). All rights reserved. ARM DDI 0479D (ID110617) Arm Cortex-M System Design Kit Technical Reference Manual Copyright © 2011, 2013, 2017 Arm Limited (or its affiliates). All rights reserved. Release Information The following changes have been made to this document: Change history Date Issue Confidentiality Change 14 March 2011 A Non-Confidential First release for r0p0 16 June 2011 B Non-Confidential Second release for r0p0 19 April 2013 C Non-Confidential First release for r1p0 31 October 2017 D Non-Confidential First release for r1p1 Proprietary Notice This document is protected by copyright and other related rights and the practice or implementation of the information contained in this document may be protected by one or more patents or pending patent applications. No part of this document may be reproduced in any form by any means without the express prior written permission of Arm. No license, express or implied, by estoppel or otherwise to any intellectual property rights is granted by this document unless specifically stated. Your access to the information in this document is conditional upon your acceptance that you will not use or permit others to use the information for the purposes of determining whether implementations infringe any third party patents. THIS DOCUMENT IS PROVIDED “AS IS”. ARM PROVIDES NO REPRESENTATIONS AND NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY, SATISFACTORY QUALITY, NON-INFRINGEMENT OR FITNESS FOR A PARTICULAR PURPOSE WITH RESPECT TO THE DOCUMENT. -
Workshop on Post-Silicon Debug: Technologies, Methodologies, and Best Practices
Wisam Kadry – IBM Research, Haifa 7 June 2012 Workshop on Post-silicon Debug: Technologies, Methodologies, and Best Practices © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop Thanks to Mr. Amir Nahir IBM Research – Haifa, Israel Received his BSc in computer science from Technion, IIT in 2005, and is currently pursuing his PhD there. He has been a research staff member at the IBM Research Labs in Haifa since 2006, and has spent most of his time leading the development of Threadmill – a post-silicon functional validation exerciser. Since the beginning of 2011, Amir manages the Post-Silicon Validation and Design Automation Group 2 © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop Agenda Session I (09:00-10:30) Wisam Kadry - IBM Haifa Research Lab., Haifa, Israel Kevin Reick - IBM Corp., Austin, TX Subhasish Mitra - Stanford Univ., Stanford, CA David Erikson - Advanced Micro Devices, Fort Collins, CO Bradley Quinton - Tektronix, Inc., Vancouver, BC, Canada Break (10:30-11:00) Session II (11:00-12:30) Alan Hu - Univ. of British Columbia, Vancouver, BC, Canada Keshavan Tiruvallur - Intel Corp., Portland, OR Nagib Hakim - Intel Corp., Santa Clara, CA Valeria Bertacco - Univ. of Michigan, Ann Arbor, MI Sharad Kumar - Freescale Semiconductor, Inc., Noida, India Lunch (12:30-13:30) Panel (13:30-15:00) Moderator: Harry Foster - Mentor Graphics Corp., Plano, TX 3 © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop More complex chips 4 © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop Observe 5 © 2012 IBM -
Navigation Techniques in Augmented and Mixed Reality: Crossing the Virtuality Continuum
Chapter 20 NAVIGATION TECHNIQUES IN AUGMENTED AND MIXED REALITY: CROSSING THE VIRTUALITY CONTINUUM Raphael Grasset 1,2, Alessandro Mulloni 2, Mark Billinghurst 1 and Dieter Schmalstieg 2 1 HIT Lab NZ University of Canterbury, New Zealand 2 Institute for Computer Graphics and Vision Graz University of Technology, Austria 1. Introduction Exploring and surveying the world has been an important goal of humankind for thousands of years. Entering the 21st century, the Earth has almost been fully digitally mapped. Widespread deployment of GIS (Geographic Information Systems) technology and a tremendous increase of both satellite and street-level mapping over the last decade enables the public to view large portions of the 1 2 world using computer applications such as Bing Maps or Google Earth . Mobile context-aware applications further enhance the exploration of spatial information, as users have now access to it while on the move. These applications can present a view of the spatial information that is personalised to the user’s current context (context-aware), such as their physical location and personal interests. For example, a person visiting an unknown city can open a map application on her smartphone to instantly obtain a view of the surrounding points of interest. Augmented Reality (AR) is one increasingly popular technology that supports the exploration of spatial information. AR merges virtual and real spaces and offers new tools for exploring and navigating through space [1]. AR navigation aims to enhance navigation in the real world or to provide techniques for viewpoint control for other tasks within an AR system. AR navigation can be naively thought to have a high degree of similarity with real world navigation. -
Parallel Range, Segment and Rectangle Queries with Augmented Maps
Parallel Range, Segment and Rectangle Queries with Augmented Maps Yihan Sun Guy E. Blelloch Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] Abstract The range, segment and rectangle query problems are fundamental problems in computational geometry, and have extensive applications in many domains. Despite the significant theoretical work on these problems, efficient implementations can be complicated. We know of very few practical implementations of the algorithms in parallel, and most implementations do not have tight theoretical bounds. In this paper, we focus on simple and efficient parallel algorithms and implementations for range, segment and rectangle queries, which have tight worst-case bound in theory and good parallel performance in practice. We propose to use a simple framework (the augmented map) to model the problem. Based on the augmented map interface, we develop both multi-level tree structures and sweepline algorithms supporting range, segment and rectangle queries in two dimensions. For the sweepline algorithms, we also propose a parallel paradigm and show corresponding cost bounds. All of our data structures are work-efficient to build in theory (O(n log n) sequential work) and achieve a low parallel depth (polylogarithmic for the multi-level tree structures, and O(n) for sweepline algorithms). The query time is almost linear to the output size. We have implemented all the data structures described in the paper using a parallel augmented map library. Based on the library each data structure only requires about 100 lines of C++ code. We test their performance on large data sets (up to 108 elements) and a machine with 72-cores (144 hyperthreads). -
Handwritten Digit Classication Using 8-Bit Floating Point Based Convolutional Neural Networks
Downloaded from orbit.dtu.dk on: Apr 10, 2018 Handwritten Digit Classication using 8-bit Floating Point based Convolutional Neural Networks Gallus, Michal; Nannarelli, Alberto Publication date: 2018 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Gallus, M., & Nannarelli, A. (2018). Handwritten Digit Classication using 8-bit Floating Point based Convolutional Neural Networks. DTU Compute. (DTU Compute Technical Report-2018, Vol. 01). General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Handwritten Digit Classification using 8-bit Floating Point based Convolutional Neural Networks Michal Gallus and Alberto Nannarelli (supervisor) Danmarks Tekniske Universitet Lyngby, Denmark [email protected] Abstract—Training of deep neural networks is often con- In order to address this problem, this paper proposes usage strained by the available memory and computational power. of 8-bit floating point instead of single precision floating point This often causes it to run for weeks even when the underlying which allows to save 75% space for all trainable parameters, platform is employed with multiple GPUs. -
High Dynamic Range Video
High Dynamic Range Video Karol Myszkowski, Rafał Mantiuk, Grzegorz Krawczyk Contents 1 Introduction 5 1.1 Low vs. High Dynamic Range Imaging . 5 1.2 Device- and Scene-referred Image Representations . ...... 7 1.3 HDRRevolution ............................ 9 1.4 OrganizationoftheBook . 10 1.4.1 WhyHDRVideo? ....................... 11 1.4.2 ChapterOverview ....................... 12 2 Representation of an HDR Image 13 2.1 Light................................... 13 2.2 Color .................................. 15 2.3 DynamicRange............................. 18 3 HDR Image and Video Acquisition 21 3.1 Capture Techniques Capable of HDR . 21 3.1.1 Temporal Exposure Change . 22 3.1.2 Spatial Exposure Change . 23 3.1.3 MultipleSensorswithBeamSplitters . 24 3.1.4 SolidStateSensors . 24 3.2 Photometric Calibration of HDR Cameras . 25 3.2.1 Camera Response to Light . 25 3.2.2 Mathematical Framework for Response Estimation . 26 3.2.3 Procedure for Photometric Calibration . 29 3.2.4 Example Calibration of HDR Video Cameras . 30 3.2.5 Quality of Luminance Measurement . 33 3.2.6 Alternative Response Estimation Methods . 33 3.2.7 Discussion ........................... 34 4 HDR Image Quality 39 4.1 VisualMetricClassification. 39 4.2 A Visual Difference Predictor for HDR Images . 41 4.2.1 Implementation......................... 43 5 HDR Image, Video and Texture Compression 45 1 2 CONTENTS 5.1 HDR Pixel Formats and Color Spaces . 46 5.1.1 Minifloat: 16-bit Floating Point Numbers . 47 5.1.2 RGBE: Common Exponent . 47 5.1.3 LogLuv: Logarithmic encoding . 48 5.1.4 RGB Scale: low-complexity RGBE coding . 49 5.1.5 LogYuv: low-complexity LogLuv . 50 5.1.6 JND steps: Perceptually uniform encoding . -
Supporting Technology for Augmented Reality Game-Based Learning
DOCTORAL THESIS Supporting Technology for Augmented Reality Game-Based Learning Hendrys Fabián Tobar Muñoz 2017 Doctorate in Technology Supervised by: PhD. Ramon Fabregat PhD. Silvia Baldiris Presented in partial fulfillment of the requirements for a doctoral degree from the University of Girona The research reported in this thesis was partially sponsored by: The financial support by COLCIENCIAS – Colombia’s Administrative Department of Science Technology and Innovation within the program of doctoral scholarships. The research reported in this thesis was carried out as part of the following projects: ARreLS project, funded by Spanish Economy and Competitiveness Ministry(TIN2011-23930) Open Co-Creation Project, funded by the Spanish Economy and Competitiveness Ministry (TIN2014-53082-R) The research reports in this thesis was developed within the research lines of the Communications and Distributed Systems research group (BCDS, ref GRCT40) which is part of the DURSI consolidated research group COMUNICACIONS I SISTEMES INTEL·LIGENTS. © Hendrys Fabián Tobar Muñoz, Girona, Catalonia, Spain, 2017 All Rights Reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without written permission granted by the author. Gracias a Dios Todopoderoso y a Su Divina Providencia. Este trabajo está dedicado a mi Reina Omaira que es lo más hermoso de este mundo. ______________________________________________________________________________________________________________________ Thanks to God Almighty and His Divine Providence. This work is dedicated to my Queen Omaira who is the most beautiful of this world (I swear, this sounds better in Spanish) ACKNOWLEDGEMENTS I have always liked games and digital technologies and I have always believed in their educational potential. -
On the Automated Derivation of Domain-Specific UML Profiles
Schriften aus der Fakultät Wirtschaftsinformatik und 37 Angewandte Informatik der Otto-Friedrich-Universität Bamberg On the Automated Derivation of Domain-Specifc UML Profles Alexander Kraas 37 Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich- Universität Bamberg Contributions of the Faculty Information Systems and Applied Computer Sciences of the Otto-Friedrich-University Bamberg Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich- Universität Bamberg Contributions of the Faculty Information Systems and Applied Computer Sciences of the Otto-Friedrich-University Bamberg Band 37 2019 On the Automated Derivation of Domain-Specifc UML Profles Alexander Kraas 2019 Bibliographische Information der Deutschen Nationalbibliothek Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliographie; detaillierte bibliographische Informationen sind im Internet über http://dnb.d-nb.de/ abrufbar. Diese Arbeit hat der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg als Dissertation vorgelegen. 1. Gutachter: Prof. Dr. Gerald Lüttgen, Otto-Friedrich-University Bamberg, Germany 2. Gutachter: Prof. Dr. Richard Paige, McMaster University, Canada Tag der mündlichen Prüfung: 13.05.2019 Dieses Werk ist als freie Onlineversion über den Publikationsserver (OPUS; http://www. opus-bayern.de/uni-bamberg/) der Universität Bamberg erreichbar. Das Werk – ausge- nommen Cover, Zitate und Abbildungen – -
PAM: Parallel Augmented Maps
PAM: Parallel Augmented Maps Yihan Sun Daniel Ferizovic Guy E. Belloch Carnegie Mellon University Karlsruhe Institute of Technology Carnegie Mellon University [email protected] [email protected] [email protected] Abstract 1 Introduction Ordered (key-value) maps are an important and widely-used The map data type (also called key-value store, dictionary, data type for large-scale data processing frameworks. Beyond table, or associative array) is one of the most important da- simple search, insertion and deletion, more advanced oper- ta types in modern large-scale data analysis, as is indicated ations such as range extraction, filtering, and bulk updates by systems such as F1 [60], Flurry [5], RocksDB [57], Or- form a critical part of these frameworks. acle NoSQL [50], LevelDB [41]. As such, there has been We describe an interface for ordered maps that is augment- significant interest in developing high-performance paral- ed to support fast range queries and sums, and introduce a lel and concurrent algorithms and implementations of maps parallel and concurrent library called PAM (Parallel Augment- (e.g., see Section 2). Beyond simple insertion, deletion, and ed Maps) that implements the interface. The interface includes search, this work has considered “bulk” functions over or- a wide variety of functions on augmented maps ranging from dered maps, such as unions [11, 20, 33], bulk-insertion and basic insertion and deletion to more interesting functions such bulk-deletion [6, 24, 26], and range extraction [7, 14, 55]. as union, intersection, filtering, extracting ranges, splitting, One particularly useful function is to take a “sum” over a and range-sums. -
Distributed Cognitions GENERAL EDITORS: ROY PEA Psychological and Educational JOHN SEELY BROWN Considerations
Learning in doing: Social, cognitive, and computational perspectives Distributed cognitions GENERAL EDITORS: ROY PEA Psychological and educational JOHN SEELY BROWN considerations The construction zone: Working for cognitive change in school Denis Newman, Peg Grrfin, and Michael Cole Plans and situated actions: The problem of human-machine Edited by interaction Lucy Suchman GAVRIEL SALOMON Situated learning: Legitimate peripheral participation University of Ha$, Israel Jean Lave and Etienne Wenger Street mathematics and school mathematics Terezinha Nunes, Anahria Dim Schliemann, and David William Carraher Understanding practice: Perspectives on activity and context Seth Chaiklin andJean Lave (editors) I943 CAMBRIDGE UNIVERSITY PRESS Published by the Press Syndicate of the University of Cambridge The Pin Building, Trumpington Street, Cambridge CB2 1RI' 40 West 20th Street, New York, NY 10011-4211, USA Contents 10 Stamford Road, Oakleigh, Melbourne 3166, Australia 0 Cambridge Universify Press 1993 First phlished 1993 Printed in the United States of America Library of Conqcsr Catalo@n~-in-PublicdionData Disaibuted cognitions : psychological and educational considerations I / edited by Gavriel Salomon. List of contribufon page vii I p. cm. - (Learning in doing) Includes index. Series foreword ix ISBN 0-521-41406-7 (hard) xi 1. Cognition and culture. 2. Knowledge, Sociology of. 3. Cognition - Social aspects. 4. Learning. Psychology of - Social aswcts. I. Salomon. Gavriel. 11. Series. 1 A cultural-historical approach to distributed 92-41220 cognition CIP MICHAEL COLE AND YRJO ENGESTR~M A catalog record for this book is available from the British Library. 2 Practices of distributed intelligence and designs for education ISBN 0-521-41406-7 hardback ROY D. PEA 3 Person-plus: a distributed view of thinking and learning D.