TOOLS & PROCESSORS FOR COMPUTER VISION. Selected Results from the Embedded Vision Alliance s Computer Vision Developer Survey

Similar documents
TOOLS & PROCESSORS FOR COMPUTER VISION. Selected Results from the Embedded Vision Alliance s Fall 2017 Computer Vision Developer Survey

TOOLS AND PROCESSORS FOR COMPUTER VISION. Selected Results from the Embedded Vision Alliance s Spring 2017 Computer Vision Developer Survey

Computer Vision at the Edge and in the Cloud: Architectures, Algorithms, Processors, and Tools

Development and Deployment of Embedded Vision in Industry: An Update. Jeff Bier, Founder, Embedded Vision Alliance / President, BDTI

Harnessing the Power of AI: An Easy Start with Lattice s sensai

Embedding Artificial Intelligence into Our Lives

Transformation to Artificial Intelligence with MATLAB Roy Lurie, PhD Vice President of Engineering MATLAB Products

GPU ACCELERATED DEEP LEARNING WITH CUDNN

KÜNSTLICHE INTELLIGENZ JOBKILLER VON MORGEN?

23270: AUGMENTED REALITY FOR NAVIGATION AND INFORMATIONAL ADAS. Sergii Bykov Technical Lead Machine Learning 12 Oct 2017

Artificial intelligence, made simple. Written by: Dale Benton Produced by: Danielle Harris

A Roadmap For Building Indigenous Digital Excellence: Looking To 2030

Challenges in Transition

DEEP DIVE ON AZURE ML FOR DEVELOPERS

Vision with Precision Webinar Series Augmented & Virtual Reality Aaron Behman, Xilinx Mark Beccue, Tractica. Copyright 2016 Xilinx

Deep learning for INTELLIGENT machines

Job Description. Commitment: Must be available to work full-time hours, M-F for weeks beginning Summer of 2018.

COMPUTER. 1. PURPOSE OF THE COURSE Refer to each sub-course.

ROAD RECOGNITION USING FULLY CONVOLUTIONAL NEURAL NETWORKS

HPC + AI. Mike Houston

THE NEXT WAVE OF COMPUTING. September 2017

AI Application Processing Requirements

Space Challenges Preparing the next generation of explorers. The Program

IHV means Independent Hardware Vendor. Example is Qualcomm Technologies Inc. that makes Snapdragon processors. OEM means Original Equipment

THE INFLUENCE OF ACADEMIC RESEARCH ON INDUSTRY R&D. Steve Keckler, Vice President of Architecture Research June 19, 2016

LEGO car course topics

Job Title: DATA SCIENTIST. Location: Champaign, Illinois. Monsanto Innovation Center - Let s Reimagine Together

LEARN REAL-TIME & EMBEDDED COMPUTING CONFERENCE. Albuquerque December 6, 2011 Phoenix December 8, Register for FREE

Table of Contents HOL EMT

Deep Learning. Dr. Johan Hagelbäck.

HARNESSING TECHNOLOGY

Analog Custom Layout Engineer

MSc(CompSc) List of courses offered in

Is housing really ready to go digital? A manifesto for change

Removing barriers from AI startups Machine Intelligence Garage

Smart Cities at CES 2018: January 9-12

VSI Labs The Build Up of Automated Driving

August 5 8, 2013 Austin, Texas. Preliminary Conference Program. Register now at ni.com/niweek or call

AI-READY OR NOT: ARTIFICIAL INTELLIGENCE HERE WE COME!

Revolutionize the Service Industries with AI 2016 Service Robot

ACCENTURE INNOVATION ARCHITECTURE USES AN INNOVATION-LED APPROACH TO HELP OUR CLIENTS DEVELOP AND DELIVER DISRUPTIVE INNOVATIONS, AND TO SCALE THEM

Neural Networks The New Moore s Law

Privacy and the EU GDPR US and UK Privacy Professionals

Developing a GPU Processing Framework for Accelerating Remote Sensing Algorithms

Navigating the AI Adoption Minefield Pitfalls, best practices, and developing your own AI roadmap April 11

Early Adopter : Multiprocessor Programming in the Undergraduate Program. NSF/TCPP Curriculum: Early Adoption at the University of Central Florida

INTEL INNOVATION GENERATION

Software Computer Vision - Driver Assistance

Implementing Vision Capabilities in Embedded Systems

REVOLUTIONIZING THE COMPUTING LANDSCAPE AND BEYOND.

Pathbreaking robots for pathbreaking research. Introducing. KINOVA Gen3 Ultra lightweight robot. kinovarobotics.com 1

BI TRENDS FOR Data De-silofication: The Secret to Success in the Analytics Economy

Guidelines to Promote National Integrated Circuit Industry Development : Unofficial Translation

The Deloitte Innovation Survey The case of Greece

Computational Efficiency of the GF and the RMF Transforms for Quaternary Logic Functions on CPUs and GPUs

March 10, Greenbelt Road, Suite 400, Greenbelt, MD Tel: (301) Fax: (301)

deepening of the professional skills

TOP TECHNOLOGY CHALLENGES AND THE RELATIONSHIP TO THE AUDIT PLAN. ISACA/Protiviti 6 th Annual IT Audit Benchmarking Survey March 15, 2017 Webinar

Executive Summary FUTURE SYSTEMS. Thriving in a world of constant change

High Performance Computing for Engineers

CALL FOR PAPERS. embedded world Conference. -Embedded Intelligence- embedded world Conference Nürnberg, Germany

DSP Valley Designing Smart Products

Tackling the Battery Problem for Continuous Mobile Vision

FDA Centers of Excellence in Regulatory and Information Sciences

Exploring Computation- Communication Tradeoffs in Camera Systems

APSEC President s Report

Architecting Systems of the Future, page 1

CIP 2018 Project Outline

Skills for Digital Transformation Research Report Patrick Hoberg Helmut Krcmar Bernd Welz. In Collaboration with

ICT4 Manuf. Competence Center

NEW GENERATION OF VCs LEADS STARTUP CEOs BACK TO THE FOLD

FOREST PRODUCTS: THE SHIFT TO DIGITAL ACCELERATES

Innovation Management and Technology Adoption. Dr. Mircea Mihaescu, P.Eng. March 7, 2012

THE VISIONLAB TEAM engineers - 1 physicist. Feasibility study and prototyping Hardware benchmarking Open and closed source libraries

Using Data Analytics and Machine Learning to Assess NATO s Information Environment

MACHINE LEARNING Games and Beyond. Calvin Lin, NVIDIA

FRAUNHOFER INSTITUTE FOR OPEN COMMUNICATION SYSTEMS FOKUS COMPETENCE CENTER VISCOM

Digital Transformation Delivering Business Outcomes

Remuneration Report

The work under the Environment under Review subprogramme focuses on strengthening the interface between science, policy and governance by bridging

SMART PLACES WHAT. WHY. HOW.

GPU-accelerated SDR Implementation of Multi-User Detector for Satellite Return Links

Proposers Day Workshop

Eyedentify MMR SDK. Technical sheet. Version Eyedea Recognition, s.r.o.

STI for reducing inequality within and among countries (SDG 10)

What we are expecting from this presentation:

BOOKMARK YOUR TALK AT STAR CONFERENCES MAY 30-31, 2019

Kornél Lehőcz Software development consultant

LEADING DIGITAL TRANSFORMATION AND INNOVATION. Program by Hasso Plattner Institute and the Stanford Center for Professional Development

Esri and Autodesk What s Next?

Great Minds. Internship Program IBM Research - China

Space Challenges Preparing the next generation of explorers. The Program

model 802C HF Wideband Direction Finding System 802C

2018 Guide to Engineering Compensation

Radio Deep Learning Efforts Showcase Presentation

SCAI SuperComputing Application & Innovation. Sanzio Bassini October 2017

Introducing Elsevier Research Intelligence

LEADING DIGITAL TRANSFORMATION AND INNOVATION. Program by Hasso Plattner Institute and the Stanford Center for Professional Development

SAMPLE PROGRAM AGENDA

Mobile Cognitive Indoor Assistive Navigation for the Visually Impaired

Transcription:

TOOLS & PROCESSORS FOR COMPUTER VISION Selected Results from the Embedded Vision Alliance s Computer Vision Developer Survey JANUARY 2019

EXECUTIVE SUMMA Y Since 2015, the Embedded Vision Alliance has surveyed computer vision developers regarding the products they are working on and the hardware and software tools they are using in their projects. This white paper provides selected results from our most recent survey, conducted in. We received responses from 692 computer vision developers across a wide range of industries, organizations, geographical locations and job types. We have focused our analysis on the 345 respondents whose organizations are developing end products for consumers, businesses or governments (vs. organizations that are providing services, or providing components, subsystems or software for incorporation into new products). ABOUT THE EMBEDDED VISION ALLIANCE The Embedded Vision Alliance is a global partnership that brings together technology providers with end product and system developers who are enabling innovative, practical applications of computer vision. Our mission is to inspire and empower product creators to incorporate visual intelligence into new products and applications, and enable member companies to accelerate success in computer vision by: We hope these selected results provide insight into the popular hardware and software platforms being used today for vision-enabled end products. Full survey results are available for Embedded Vision Alliance member companies. Please email info@embedded-vision.com for more information. Note: Percentages add up to more than 100% in many of the charts presented in this white paper. This is because many of the questions allowed respondents to select more than one option. Bringing together suppliers, end-product designers and partners to speed the adoption of computer vision in products Delivering timely insights into market research, technology trends, standards and application requirements Enabling companies to become more visible as thought leaders For information on joining the Alliance, please visit www.embedded-vision.com 2019 Embedded Vision Alliance. All rights reserved. 1

CU ENTLY USING OR PLANNING TO USE COMPUTE VISION IN YOU P ODUCTS OR SE VICES 74% +3 Currently using 19% Planning to use -1 4% Don t know 3% -2 Not planning to use More than 90% of survey respondents are using or planning to use computer vision in their products or services. Similar to our previous surveys, the overwhelming majority of respondents are developing or planning to develop products or services using computer vision. 2

TYPES OF P OCESSO S USED FO VISION TASKS Ranked as One of Top Three Dedicated deep-learning processors gained significant 100 90 80 70 60 50 76% 62% +2 popularity last year. Due to their specialized architectures, they often deliver extremely high cost and energy efficiencies on deep neural network inference tasks. However, in many cases a companion processor is needed to run non-neural network algorithms and other functions. 40 30 34% -6 32% 27% 26% 24% 20 +3-1 -5 10 0 CPU GPU FPGA Dedicated deep-learning processor Mobile Visionspecific DSP 3

PROG AMMING LANGUAGES USED FO C EATING AND IMPLEMENTING NON-NEU AL NETWO K VISION ALGO ITHMS Ranked as One of Top Three C++ Python 46% 46% C 44% MATLAB 27% CUDA Java OpenCL Objective-C OpenGL* 4% Halide* 2% Other 14% 15% 18% 11% 10% 11% 11% 3% 2% 7% 12% 61% 75% 75% C++ remains the most popular language for computer vision development and implementation. Creation Implementation 10 20 30 40 50 60 70 80 90 100 Note: This question was rephrased for the Survey. The current survey asks two separate questions regarding vision algorithms: one about algorithm creation and one about algorithm implementation. Since OpenGL and Halide are generally implementation languages, they were not answer options for the algorithm creation question. This was a sufficiently large change that comparison with answers from our last survey was not meaningful and are thus not shown. 4

LIB A IES AND APIs USED FO IMPLEMENTING NON-NEU AL NETWO K VISION TASKS Ranked as One of Top Three 100 90 80 70 60 50 89% OpenCV maintains its dominant role in implementation of vision tasks. 40 30 24% 9% 20 10-12 14% 12% -5 10% -12 5% 1% -3 0 OpenCV OpenGL Dlib* FastCV OpenVX Halide* OrbSLAM* Other Note: For the survey, we added 3 new answer options, marked with an asterisk. This can have a general effect of reducing percentage numbers across older categories. 5

USE OF NEU AL NETWO KS FO COMPUTE VISION 40% Yes, extensively 19% +5 +3 Yes, in a minor role 11% No 2% -2 Don t know 28% -6 Not yet, but planning to 87% of survey respondents use or plan to use neural networks to perform computer vision functions in their products or services. Neural networks continue to be one of the dominant techniques for computer vision. 6

SOFTWA E USED FO C EATING, T AINING O EVALUATING NEU AL NETWO KS FO VISION TASKS Ranked as One of Top Three TensorFlow continues to dominate the field. Google s open-source TensorFlow increased its lead as the most popular deep learning/neural network design framework. TensorFlow 84% Caffe Caffe2 Torch 22% Custom In-House MATLAB NVIDIA DIGITS 6% Theano Neon Other 2% 5% MXNet 5% -5-5 -7-1 18% 18% 18% 38% 29% -3-9 -6 +11 +14-20 While Caffe and Caffe 2 remain popular, Torch has made significant inroads over the past year. +19 10 20 30 40 50 60 70 80 90 100 7

SOFTWA E USED FO DEPLOYING NEU AL NETWO KS FO VISION TASKS Ranked as One of Top Three 100 90 80 Approaches continue to vary for deploying neural networks (as opposed to creating, training or evaluating them). Similar to the pattern for development, however, TensorFlow s popularity increased, as did the OpenCV DNN module. 70 62% 60 +15 50 40 41% +11 34% 31% 30-4 -19 24% 20-4 -4 12% 10 5% +8 TensorFlow OpenCV DNN module Custom In-House Caffe Processor Vendor-Provided Tools/Libraries OpenVX NN Extension Other 8

OTHE ALLIANCE P OG AMS VISION ACCELE ATO PROG AM The Vision Accelerator Program helps companies quickly understand and navigate the technical and business complexity of incorporating visual perception capabilities so they can more quickly and confidently plan, develop and deliver their products. It is a service available to members of the Embedded Vision Alliance who are developing end products and systems with visual perception capabilities (e.g., deep learning, 3D sensing). The Vision Accelerator Program helps companies: Make decisions in a fast-changing market where areas like deep learning and 3D sensing are rapidly moving from research into practical use Understand the tradeoffs for low-power, low-cost devices and cloud processing Know what vision software standards, open source tools and algorithms are gaining traction Identify which startups, suppliers, partners and experts have relevant vision technologies and know-how Build skills and recruit the right talent Access and develop a network of experts, suppliers and partners For more information on the Vision Accelerator Program, email accelerate@embedded-vision.com EMBEDDED VISION SUMMIT The Embedded Vision Summit, held in Silicon Valley every year in May, is the only event focused exclusively on the technologies, hardware and software that bring visual intelligence to products. The Summit presents the latest practical techniques and technologies for vision-based product development, and illuminates the commercial landscape, trends and business opportunities in this fast-growing market. The Summit inspires participants to use vision technology in new ways and empowers them with the know-how they need to integrate vision capabilities into products. The 2019 event will feature more than 90 expert presenters in 4 conference tracks covering every aspect of computer vision. The event s Vision Technology Showcase will include more than 100 demonstrations of commercially-available computer vision components and solutions both in hardware and software from more than 50 top suppliers. Day 3 of the conference consists of in-depth Vision Technology Workshops presented by Alliance Member companies. For more information on the 2019 Embedded Vision Summit, please visit www.embeddedvisionsummit.com 9

1646 North California Blvd Suite 220 Walnut Creek, CA 94596 USA Phone: +1 (925) 954-1411 Fax: +1 (925) 954-1423 info@embedded-vision.com