DOCSLIB.ORG

ART Vs. NDK Vs. GPU Acceleration: a Study of Performance of Image Processing Algorithms on Android

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
ART Vs. NDK Vs. GPU Acceleration: a Study of Performance of Image Processing Algorithms on Android DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2017 ART vs. NDK vs. GPU acceleration: A study of performance of image processing algorithms on Android ANDREAS PÅLSSON KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION ART vs. NDK vs. GPU acceleration: A study of performance of image processing algorithms on Android ANDREAS PÅLSSON Master in Computer Science Date: June 26, 2017 Supervisor: Cyrille Artho Examiner: Johan Håstad Swedish title: ART, NDK eller GPU acceleration: En prestandastudie av bildbehandlingsalgoritmer på Android School of Computer Science and Communication iii Abstract The Android ecosystem contains three major platforms for execution suit- able for different purposes. Android applications are normally written in the Java programming language, but computationally intensive parts of An- droid applications can be sped up by choosing to use a native language or by utilising the parallel architecture found in graphics processing units (GPUs). The experiments conducted in this thesis measure the performance benefits by switching from Java to C++ or RenderScript, Google’s GPU acceleration framework. The experiments consist of often-done tasks in image processing. For some of these tasks, optimized libraries and implementations already exist. The performance of the implementations provided by third parties are compared to our own. Our results show that for advanced image processing on large images, the benefits are large enough to warrant C++ or RenderScript usage instead of Java in modern smartphones. However, if the image processing is conducted on very small images (e.g. thumbnails) or the image processing task contains few calculations, moving to a native language or RenderScript is not worth the added development time and static complexity. RenderScript is the best choice if the GPU vendors provide an optimized implementation of the processing task. If there is no such implementation provided, both C++ and RenderScript are viable choices. If full precision is required in the floating point arithmetic, a C++ implementation is the rec- ommended. If it is possible to achieve the desired effect without compliance with IEEE Floating Point Arithmetic standard, RenderScript provides better run time performance. iv Sammanfattning Android-ekosystemet innehåller tre exekveringsplattformer passande för oli- ka syften. Android-applikationer är vanligtvis skrivna i programmerings- språket Java, men beräkningsintensiva delar av en Android-applikation kan snabbas upp genom att använda en statiskt kompilerat språk eller genom att utnyttja den parallella arkitekturen som hittas i grafikprocessorer. Experi- menten utförda i det här projektet ämnar mäta prestandasförbättringar som kan uppnås genom att byta från Java till C++ eller RenderScript, Googles grafikaccelerationsramverk. Experimenten består av ofta använda algoritmer inom bildhantering. För någ- ra av dessa finns det optimerade bibliotek och övriga färdiga implementatio- ner. Prestandan av tredjepartsbiblioteken jämförs med våra implementatio- ner. Våra resultat visar att för avancerad bildhantering är prestandaförbättringar- na tillräckligt bra för att använda C++ eller RenderScript istället för Java på moderna smartphones. I de fall bildhanteringen görs på väldigt små bilder eller innehåller få beräkningar (exempelvis miniatyrbilder) är bytet från Ja- va till RenderScript eller C++ inte värt den extra utvecklingstiden samt den statiska kodkomplexiteten. RenderScript är det bästa valet då grafikprocessortillverkarna tillhandahål- ler implementationer av algoritmen som ska köras. Om det inte finns någon sådan implementation är både C++ och RenderScript tillämpbara val. Om noggrann precision krävs rekommenderas en C++-implementation. Däremot om full precision inte behövs vid flyttalsberäkningar rekommenderas istället RenderScript. Contents 1 Introduction 1 1.1 Problem . .2 1.2 Research Question . .2 1.3 Scope . .3 1.4 Ethics and sustainability . .3 1.5 Structure . .3 2 Background 4 2.1 Native and interpreted languages . .4 2.1.1 Java . .4 2.1.2 C++ . .5 2.1.3 Performance . .6 2.2 Android . .6 2.3 Android application compilation . .7 2.4 Dalvik . .8 2.5 Android Runtime . .8 2.5.1 Ahead-of-time (AOT) compilation . .9 2.5.2 Improved garbage collection . 10 2.5.3 Just-in-time (JIT) compilation . 10 2.6 Android Native Development Kit . 11 2.7 RenderScript . 11 2.7.1 Compilation and deployment . 12 2.7.2 Floating point precision . 13 2.8 Image Processing . 14 2.8.1 Image smoothing . 14 2.8.2 Grayscaling . 16 2.8.3 Thresholding . 17 2.9 Color spaces . 18 3 Related Work 19 3.1 Java and C++ benchmarks . 19 v vi CONTENTS 3.2 Dalvik vs ART . 20 3.3 Using GPU for calculations . 21 3.3.1 RenderScript . 21 3.3.2 OpenCL . 21 4 Method 23 4.1 Choice of method and algorithms . 23 4.2 Development environment and devices . 23 4.3 Implementation . 24 4.3.1 Color space conversion . 25 4.3.2 Blurring . 27 4.3.3 Grayscaling and thresholding . 30 4.4 Measuring Runtime Performance . 30 4.4.1 Image processing . 30 4.4.2 Setup . 31 4.5 Verifying results . 32 5 Results 34 5.1 Color space conversion . 34 5.2 Blurring . 35 5.2.1 Box filter . 35 5.2.2 Median filter . 36 5.2.3 Gaussian filter . 37 5.3 Grayscaling . 38 5.4 Thresholding . 39 6 Discussion 40 6.1 Color space conversion . 40 6.2 Blurring . 41 6.3 Grayscaling and thresholding . 42 6.4 Overall Performance . 43 6.5 Threats to validity . 45 6.5.1 Choice of algorithms . 45 6.5.2 High variance . 46 6.5.3 Devices . 46 6.5.4 Image sizes . 46 6.5.5 Optimization . 46 6.6 Future Research . 47 7 Conclusion 49 Bibliography 50 CONTENTS vii A Tables 53 A.1 Blurring . 53 A.1.1 Box filter . 55 A.1.2 Median filter . 57 A.1.3 Gaussian filter . 60 A.2 Grayscaling . 63 A.3 Thresholding . 64 Chapter 1 Introduction The first version of the mobile operating system Android was released in fall 2008. It is, as of January 2017, the most widely used smartphone operating system [14]. It is used all over the world, with varying device and network quality. Because of these reasons, it is important to mobile application de- velopers to be able to develop high quality applications that work well on low-end devices in third world countries. Android application developers can choose to write the business logic of an application in a native language (a source language that is directly compiled to machine code) or Java, where Google recommends the use of Java [8]. However, when conducting computationally intensive tasks it can be advan- tageous to use native languages, as it is generally faster than Java [8], to not impede the user experience. Moreover, a developer can utilize GPU (graphics processing unit) accelerated computing to utilize the full capabilities of the device. This means using the device’s graphics processor to offload compute-intensive portions of code to the GPU, while the remainder of the code remains on the CPU (central pro- cessing unit). This allows the device to take advantage of the massively par- allel architecture of the GPU. Code written to run on a GPU does not have to be custom-written for each different type of GPU, but can be compiled from a higher-level language. This means that GPU acceleration is more readily available for developers today than what it traditionally has been. 1 2 CHAPTER 1. INTRODUCTION As today’s users of technological products see more and more of virtual and augmented reality products, it is of utmost importance to keep the experience as smooth as possible. Many new technologies offer a more visual experience than before, which further increases the need for performance, since graphics processing require large amounts of heavy calculations. 1.1 Problem Java is the recommended programming language for building Android ap- plications. However, the Java programming language contains features de- signed to improve safety and convenience at the expense of performance, e.g., the automatic memory management. Therefore, Google suggests that it might be useful for a developer to use a native language over Java in two cases [8]: • Squeeze extra performance out of a device to achieve low latency or run computationally intensive applications, such as games or physics simulations. • Reuse your own or other developers’ C or C++ libraries. This thesis intends to examine the first bullet point and investigate how large the performance benefits can be when conducting real time image processing. Furthermore, the usage of GPU acceleration can provide greater performance improvements due to increased levels of parallelization in the hardware. The problem is that with increased performance from the Android system, it is hard to know whether the performance benefit of using a different language than Java is worth the extra complexity needed to add another programming language to a software project. Image processing contains many computationally intensive processes and is therefore a candidate where it might be useful to switch to a native language or a framework that allows use of GPU acceleration. 1.2 Research Question The question this thesis intends to answer is the following: CHAPTER 1. INTRODUCTION 3 Can performance increases in run time warrant the usage of C++ or GPU acceleration frameworks over Java when writing image processing algorithms on Android? 1.3 Scope The reason a developer might not want to choose a native language or a GPU acceleration framework over Java despite performance benefits is likely that the added performance improvements do not outweigh the complex- ity added to the software project. This project does not intend to extensively measure the code development complexity added by using these components in an Android project. 1.4 Ethics and sustainability The work presented in this thesis aims to be as ethical as possible, in the sense that all results in presented in this thesis are reproducible from the descrip- tion in chapter 4.
Load more

Recommended publications
  • AVG Android App Performance and Trend Report H1 2016
    AndroidTM App Performance & Trend Report H1 2016 By AVG® Technologies Table of Contents Executive Summary .....................................................................................2-3 A Insights and Analysis ..................................................................................4-8 B Key Findings .....................................................................................................9 Top 50 Installed Apps .................................................................................... 9-10 World’s Greediest Mobile Apps .......................................................................11-12 Top Ten Battery Drainers ...............................................................................13-14 Top Ten Storage Hogs ..................................................................................15-16 Click Top Ten Data Trafc Hogs ..............................................................................17-18 here Mobile Gaming - What Gamers Should Know ........................................................ 19 C Addressing the Issues ...................................................................................20 Contact Information ...............................................................................21 D Appendices: App Resource Consumption Analysis ...................................22 United States ....................................................................................23-25 United Kingdom .................................................................................26-28
    [Show full text]
  • Embedded Android
    www.it-ebooks.info www.it-ebooks.info Praise for Embedded Android “This is the definitive book for anyone wanting to create a system based on Android. If you don’t work for Google and you are working with the low-level Android interfaces, you need this book.” —Greg Kroah-Hartman, Core Linux Kernel Developer “If you or your team works on creating custom Android images, devices, or ROM mods, you want this book! Other than the source code itself, this is the only place where you’ll find an explanation of how Android works, how the Android build system works, and an overall view of how Android is put together. I especially like the chapters on the build system and frameworks (4, 6, and 7), where there are many nuggets of information from the AOSP source that are hard to reverse-engineer. This book will save you and your team a lot of time. I wish we had it back when our teams were starting on the Frozen Yogurt version of Android two years ago. This book is likely to become required reading for new team members working on Intel Android stacks for the Intel reference phones.” —Mark Gross, Android/Linux Kernel Architect, Platform System Integration/Mobile Communications Group/Intel Corporation “Karim methodically knocks out the many mysteries Android poses to embedded system developers. This book is a practical treatment of working with the open source software project on all classes of devices, beyond just consumer phones and tablets. I’m personally pleased to see so many examples provided on affordable hardware, namely BeagleBone, not just on emulators.” —Jason Kridner, Sitara Software Architecture Manager at Texas Instruments and cofounder of BeagleBoard.org “This book contains information that previously took hundreds of hours for my engineers to discover.
    [Show full text]
  • A Scalable and Reliable Mobile Code Offloading Solution
    Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 8715294, 18 pages https://doi.org/10.1155/2018/8715294 Research Article MobiCOP: A Scalable and Reliable Mobile Code Offloading Solution José I. Benedetto ,1 Guillermo Valenzuela ,1 Pablo Sanabria,1 Andrés Neyem ,1 Jaime Navón,1 and Christian Poellabauer 2 1 Computer Science Department, Pontifcia Universidad Catolica´ de Chile, Santiago, Chile 2Computer Science and Engineering Department, University of Notre Dame, Notre Dame, IN, USA Correspondence should be addressed to Jose´ I. Benedetto; [email protected] Received 26 September 2017; Accepted 12 December 2017; Published 9 January 2018 Academic Editor: Konstantinos E. Psannis Copyright©2018Jose´ I. Benedetto et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Code ofoading is a popular technique for extending the natural capabilities of mobile devices by migrating processor-intensive tasks to resource-rich surrogates. Despite multiple platforms for ofoading being available in academia, these frameworks have yet to permeate the industry. One of the primary reasons for this is limited experimentation in practical settings and lack of reliability, scalability, and options for distribution. Tis paper introduces MobiCOP, a new code ofoading framework designed from the ground up with these requirements in mind. It features a novel design fully self-contained in a library and ofers compatibility with most stock Android devices available today. Compared to local task executions, MobiCOP ofers performance improvements of up to 17x and increased battery efciency of up to 25x, shows minimum performance degradation in environments with unstable networks, and features an autoscaling module that allows its server counterpart to scale to an arbitrary number of ofoading requests.
    [Show full text]
  • Performance Analysis of Mobile Applications Developed with Different Programming Tools
    MATEC Web of Conferences 252, 05022 (2019) https://doi.org/10.1051/matecconf/201925205022 CMES’18 Performance analysis of mobile applications developed with different programming tools Jakub Smołka1,*, Bartłomiej Matacz2, Edyta Łukasik1, and Maria Skublewska-Paszkowska1 1Lublin University of Technology, Electrical Engineering and Computer Science Faculty, Institute of Computer Science, Nadbystrzycka 36b, 20-618 Lublin, Poland 2 Cybercom, Hrubieszowska 2, 01-209 Warsaw, Poland Abstract. This study examines the efficiency of certain software tasks in applications developed using three frameworks for the Android system: Android SDK, Qt and AppInventor. The results obtained using the Android SDK provided the benchmark for comparison with other frameworks. Three test applications were implemented. Each of them had the same functionality. Performance in the following aspects was tested: sorting a list of items using recursion by means of the Quicksort algorithm, access time to a location from a GPS sensor, duration time for reading the entire list of phone contacts, saving large and small files, reading large and small files, image conversion to greyscale, playback time of a music file, including the preparation time. The results of the Android SDK are good. Unexpectedly, it is not the fastest tool, but the time for performing most operations can be considered satisfactory. The Qt framework is overall about 34% faster than the Android SDK. The worst in terms of overall performance is the AppInventor: it is, on average, over 626 times slower than Android SDK. 1 Introduction The third part presents the results, which are then analysed. According to a recent study [1], the number of As already mentioned, currently the most popular smartphone users is forecasted to grow from 2.1 billion system for mobile devices is Android.
    [Show full text]
  • Renderscript Basic Tutorial for Android* OS
    RenderScript Basic Tutorial for Android* OS User's Guide Compute Code Builder - Samples Document Number: 330186-002US Android RenderScript Tutorial Contents Contents ......................................................................................................................... 2 Legal Information ............................................................................................................ 3 Introduction .................................................................................................................... 4 What is RenderScript? ...................................................................................................... 4 Running and Controlling the Tutorial .................................................................................. 4 RenderScript Java* APIs ................................................................................................... 5 RenderScript Kernels ........................................................................................................ 7 Tutorial Application Structure ............................................................................................ 8 Seperating RenderScript Kernel and UI Thread Executions .................................................... 8 Android Application Lifecycle Events ................................................................................... 8 Limitations ...................................................................................................................... 9 Notes on Threading
    [Show full text]
  • Renderscript Lecture 10
    Renderscript Lecture 10 Android Native Development Kit 6 May 2014 NDK Renderscript, Lecture 10 1/41 RenderScript RenderScript Compute Scripts RenderScript Runtime Layer Reflected Layer Memory Allocation Bibliography Keywords NDK Renderscript, Lecture 10 2/41 Outline RenderScript RenderScript Compute Scripts RenderScript Runtime Layer Reflected Layer Memory Allocation Bibliography Keywords NDK Renderscript, Lecture 10 3/41 Android NDK I NDK - perform fast rendering and data-processing I Lack of portability I Native lib that runs on ARM won't run on x86 I Lack of performance I Hard to identify (CPU/GPU/DSP) cores and run on them I Deal with synchronization I Lack of usability I JNI calls may introduce bugs I RenderScript developed to overcome these problems NDK Renderscript, Lecture 10 4/41 RenderScript I Native app speed with SDK app portability I No JNI needed I Language based on C99 - modern dialect of C I Pair of compilers and runtime I Graphics engine for fast 2D/3D rendering I Deprecated from Android 4.1 I Developers prefer OpenGL I Compute engine for fast data processing I Running threads on CPU/GPU/DSP cores I Compute engine only on CPU cores NDK Renderscript, Lecture 10 5/41 RenderScript Architecture I Android apps running in the Dalvik VM I Graphics/compute scripts run in the RenderScript runtime I Communication between them through instances of reflected layer classes I Classes are wrappers around the scripts I Generated using the Android build tools I Eliminate the need of JNI I Memory management I App allocated memory I Memory
    [Show full text]
  • Android™ Hacker's Handbook
    ffi rs.indd 01:50:14:PM 02/28/2014 Page ii Android™ Hacker’s Handbook ffi rs.indd 01:50:14:PM 02/28/2014 Page i ffi rs.indd 01:50:14:PM 02/28/2014 Page ii Android™ Hacker’s Handbook Joshua J. Drake Pau Oliva Fora Zach Lanier Collin Mulliner Stephen A. Ridley Georg Wicherski ffi rs.indd 01:50:14:PM 02/28/2014 Page iii Android™ Hacker’s Handbook Published by John Wiley & Sons, Inc. 10475 Crosspoint Boulevard Indianapolis, IN 46256 www.wiley.com Copyright © 2014 by John Wiley & Sons, Inc., Indianapolis, Indiana ISBN: 978-1-118-60864-7 ISBN: 978-1-118-60861-6 (ebk) ISBN: 978-1-118-92225-5 (ebk) Manufactured in the United States of America 10 9 8 7 6 5 4 3 2 1 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, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or autho- rization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions. Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifi cally disclaim all warranties, including without limitation warranties of fi tness for a particular purpose.
    [Show full text]
  • Performance Portability for Embedded Gpus
    Performance Portability for Embedded GPUs Simon McIntosh-Smith [email protected] Head of Microelectronics Research University of Bristol, UK 1 ! GPUcompute on Embedded GPUs Simon McIntosh-Smith [email protected] Head of Microelectronics Research University of Bristol, UK 2 ! ! " Embedded GPUs becoming monsters Average Moore’s Law = 2x/2yrs 2x/3yrs 6-7B transistors 2x/2yrs ~1-2B transistors High-performance MPU, e.g. Apple's 2013 A7 Intel Nehalem processor is over 1B transistors in 102mm2 Cost-performance MPU, e.g. Nvidia Tegra http://www.itrs.net/Links/2011ITRS/2011Chapters/2011ExecSum.pdf 3 ! " Programmable embedded GPUs go mainstream in 2014 •" Lots of significant announcements recently (at CES etc) •" Imagination Series 6XT (Rogue) GPU •" ARM Mali T760 •" Qualcomm Adreno 420 •" AMD Radeon E6XXX (576 GFLOPS), Kaveri •" Intel IRIS •" Vivante GC7000 series •" Nvidia Tegra K1 •" Other vendors too: Broadcom, … •" 2014 is the year that OpenCL programmable embedded GPUs will become ubiquitous 4 ! "Architectural trends •" Integrated CPU/GPU with shared virtual memory and cache coherency •" SVM is a key new feature of OpenCL 2.0 •" Efficiency improvements in overheads for launching tasks/kernels •" Promotes finer-grained heterogeneous parallel programs •" Supported by OpenCL 2.0 subgroups and nested (dynamic) parallelism •" Heterogeneous Systems Architecture (HSA) etc 5 ! "Where are we now? •" Usefully (OpenCL) programmable GPUs just starting to become available •" OpenCL 2.0, SPIR and HSA will enable much greater efficiency in the exploitation
    [Show full text]
  • Intel® Technology Journal | Volume 18, Issue 2, 2014
    Intel® Technology Journal | Volume 18, Issue 2, 2014 Publisher Managing Editor Content Architect Cory Cox Stuart Douglas Paul Cohen Program Manager Technical Editor Technical Illustrators Paul Cohen David Clark MPS Limited Technical and Strategic Reviewers Bruce J Beare Tracey Erway Michael Richmond Judi A Goldstein Kira C Boyko Miao Wei Paul De Carvalho Ryan Tabrah Prakash Iyer Premanand Sakarda Ticky Thakkar Shelby E Hedrick Intel® Technology Journal | 1 Intel® Technology Journal | Volume 18, Issue 2, 2014 Intel Technology Journal Copyright © 2014 Intel Corporation. All rights reserved. ISBN 978-1-934053-63-8, ISSN 1535-864X Intel Technology Journal Volume 18, Issue 2 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, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4744. Requests to the Publisher for permission should be addressed to the Publisher, Intel Press, Intel Corporation, 2111 NE 25th Avenue, JF3-330, Hillsboro, OR 97124-5961. E-Mail: [email protected]. Th is publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in professional services. If professional advice or other expert assistance is required, the services of a competent professional person should be sought.
    [Show full text]
  • Optimizing Android in the ARM Ecosystem [email protected] ARM Strategic Software Alliances
    Optimizing Android in the ARM Ecosystem [email protected] ARM Strategic Software Alliances 1 ARM Engineering Global Coverage [email protected] 2 ARM Android Ecosystem Strategy § Deliver value throughout the growing Ecosystem § Make it easy for ARM Silicon Partners to Deliver Android § Off-the-shelf ARM Processor/Board ports and recipes § Make Android better on ARM for OEM’s § Optimize Key Open Source ingredients § Engage Developers in use of advanced ARM Tech § Blog Posts, Webinars § Tools, Libraries § Developer Knowledge Sites § Developer Relations 3 ARM Android Ecosystem Strategy § Deliver value throughout the growing Ecosystem § Make it easy for ARM Silicon Partners to Deliver Android § Off-the-shelf ARM Processor/Board ports and recipes § Make Android better on ARM for OEM’s § Optimize Key Open Source ingredients § Engage Developers in use of advanced ARM Tech § Blog Posts, Webinars § Tools, Libraries § Developer Knowledge Sites § Developer Relations 4 Android Boot Recipe for new ARM SoC’s § ARM brings the latest Android releases up on latest SoC’s § “From Zero to Boot” Recipe Blog Post § ARM Partners can easily apply recipe to their SoC § Typical bring-up times are in the order of a few days! 5 Android Bring-Up on ARM SoC’s Info § From Zero to Boot recipe § http://blogs.arm.com/software-enablement/498-from-zero-to-boot-porting-android- to-your-arm-platform/ § Building Android for ARM Boards - Existing ports description § http://linux-arm.org/LinuxKernel/LinuxAndroidPlatform § Git repos - existing ports § Linux Kernel and Android
    [Show full text]
  • Phase-Based Accurate Power Modeling for Mobile Application Processors
    electronics Article Phase-Based Accurate Power Modeling for Mobile Application Processors Kitak Lee 1,2 , Seung-Ryeol Ohk 1, Seong-Geun Lim 1 and Young-Jin Kim 1,* 1 Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea; [email protected] (K.L.); [email protected] (S.-R.O.); [email protected] (S.-G.L.) 2 System LSI Business, Samsung Electronics Co., Ltd., Hwaseong 18448, Korea * Correspondence: [email protected] Abstract: Modern mobile application processors are required to execute heavier workloads while the battery capacity is rarely increased. This trend leads to the need for a power model that can analyze the power consumed by CPU and GPU at run-time, which are the key components of the application processor in terms of power savings. We propose novel CPU and GPU power models based on the phases using performance monitoring counters for smartphones. Our phase-based power models employ combined per-phase power modeling methods to achieve more accurate power consumption estimations, unlike existing power models. The proposed CPU power model shows estimation errors of 2.51% for ARM Cortex A-53 and 1.97% for Samsung M1 on average, and the proposed GPU power model shows an average error of 8.92% for the Mali-T880. In addition, we integrate proposed CPU and GPU models with the latest display power model into a holistic power model. Our holistic power model can estimate the smartphone0s total power consumption with an error of 6.36% on average while running nine 3D game benchmarks, improving the error rate by about 56% compared with the latest prior model.
    [Show full text]
  • Arxiv:1602.04868V1 [Cs.CV] 16 Feb 2016
    Deep Feature-based Face Detection on Mobile Devices Sayantan Sarkar1, Vishal M. Patel2 and Rama Chellappa1 1 Center for Automation Research, University of Maryland, College Park, MD 20742 fssarkar2, [email protected] 2 Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854 [email protected] Abstract databases are predominantly composed of general surveil- lance or media type images and not specifically of images We propose a deep feature-based face detector for mo- captured using front-facing cameras of smartphones. As bile devices to detect user’s face acquired by the front- we shall discuss later, face images captured using the front- facing camera. The proposed method is able to detect faces facing cameras of mobile devices possess some unique fea- in images containing extreme pose and illumination varia- tures that can be used as powerful prior information to tions as well as partial faces. The main challenge in devel- simplify the task of face detection on mobile platforms. oping deep feature-based algorithms for mobile devices is This paper proposes a deep convolutional neural network the constrained nature of the mobile platform and the non- (DCNN)-based face detection scheme for mobile platforms. availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the 1.1. Motivation images captured by the front-facing camera of mobile de- State of the art face detection techniques are based vices and exploits the GPUs present in mobile devices with- on DCNNs [17], [18]. Variations of DCNNs have been out CUDA-based frameworks, to meet these challenges.
    [Show full text]