May 2019 Embedded Vision Summit Replay

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Conference Presentation Slides

2019 Embedded Vision Summit Slides
(183 MB—click to download.)

Conference Overview Presentations

Welcome Remarks and Conference Overview (Day 1)
Jeff Bier, Embedded Vision Alliance

Welcome Remarks and Conference Overview (Day 2)
Jeff Bier, Embedded Vision Alliance

Keynote Presentations

"The Future of Computer Vision and Machine Learning is Tiny"
Pete Warden, Google

"Making the Invisible Visible: Within Our Bodies, the World Around Us and Beyond"
Ramesh Raskar, MIT Media Lab

Business Insights Track Presentations and Interviews

"Shifts in the Automated Driving Industry"
László Kishonti, AImotive

"The Reality of Spatial Computing: What’s Working in 2019 (And Where It Goes From Here)"
Tim Merel, Digi-Capital

"Visual AI Applications and Technologies: Trends and Opportunities"
Jeff Bier, Embedded Vision Alliance and BDTI

"Embedded Vision Applications Lead Way for Processors in AI: A Market Analysis of Vision Processors"
Tom Hackenberg, IHS Markit

"Visual AI Enables Autonomous Security"
William "Bill" Santana Li, Knightscope

"Three Key Factors for Successful AI Projects"
Bruce Tannenbaum, MathWorks

"Processor Options for Edge Inference: Options and Trade-offs"
Raj Talluri, Micron Technology

"Addressing Corner Cases in Embedded Computer Vision Applications"
David Julian, Netradyne

"Automotive Vision Systems— Seeing the Way Forward"
Ian Riches, Strategy Analytics

"Making Cars That See–Failure is Not an Option"
Burkhard Huhnke, Synopsys

"Designing Your Next Vision Product Using a Systems Approach"
Ben Bodley, Teknique

"Enabling the Next Kitchen Experience Through Embedded Vision"
Sugosh Venkataraman, Whirlpool

"What’s Changing in Autonomous Vehicle Investments Worldwide—and Why?"
Rudy Burger, Woodside Capital

"AI Is Moving to the Edge—What’s the Impact on the Semiconductor Industry?"
Yohann Tschudi, Yole Développement

Vision Tank Competition Finalist Presentations
BlinkAI Technologies, Entropix, Robotic Materials, Strayos and Vyrill

Enabling Technologies Track Presentations

"Accessing Advanced Image Processing Feature Sets with Alvium Cameras Using a V4L2/GenICam Hybrid Driver"
Sebastian Günther, Allied Vision Technologies

"Tools and Techniques for Optimizing DNNs on Arm-based Processors with Au-Zone’s DeepView ML Toolkit"
Sébastien Taylor, Au-Zone Technologies

"Using Blockchain to Create Trusted Embedded Vision Systems"
Thies Möller, Basler

"Highly Efficient, Scalable Vision and AI Processors IP for the Edge"
Pulin Desai, Cadence

"Deploying Visual SLAM in Low-power Devices"
Ben Weiss, CEVA

"Memory-centric Hardware Acceleration for Machine Intelligence"
Sylvain Dubois, Crossbar

"Using TensorFlow Lite to Deploy Deep Learning on Cortex-M Microcontrollers"
Pete Warden, Google

"Emerging Processor Architectures for Deep Learning: Options and Trade-offs"
Orr Danon, Hailo

"REAL3 Time of Flight: A New Differentiator for Mobile Phones"
Walter Bell, Infineon Technologies

"Building AI Cameras with Intel Movidius VPUs"
Gary Brown, Intel

"Applied Depth Sensing with Intel RealSense"
Sergey Dorodnicov, Intel

"How to Get the Best Deep Learning Performance with the OpenVINO Toolkit"
Yury Gorbachev, Intel

"AI+: Combining AI and Other Critical Functions Using Intel FPGAs"
Ronak Shah, Intel

"Commercial Grade SLAM Frameworks for Indoor and Outdoor Applications"
John Williams, Kudan

"Accelerate Adoption of AI at the Edge with Easy to Use, Low-power Programmable Solutions"
Hussein Osman, Lattice Semiconductor

"Game Changing Depth Sensing Technique Enables Simpler, More Flexible 3D Solutions"
Takeo Miyazawa, Magik Eye

"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB"
Sandeep Hiremath and Bill Chou, MathWorks

"MediaTek’s Approach for Edge Intelligence"
Bing Yu, MediaTek

"Using High-level Synthesis to Bridge the Gap Between Deep Learning Frameworks and Custom Hardware Accelerators"
Michael Fingeroff, Mentor

"Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling"
Mike Henry, Mythic

"Neuromorphic Event-based Vision: From Disruption to Adoption at Scale"
Luca Verre, Prophesee

"Efficient Deployment of Quantized ML Models at the Edge Using Snapdragon SoCs"
Felix Baum, Qualcomm

"Snapdragon Hybrid Computer Vision/Deep Learning Architecture for Imaging Applications"
Robert Lay, Qualcomm

"Dynamically Reconfigurable Processor Technology for Vision Processing"
Yoshio Sato, Renesas

"Accelerating Smart Camera Time to Market Using a System-on-module Approach"
Ian Billing, Teknique

"Machine Learning at the Edge in Smart Factories Using TI Sitara Processors"
Manisha Agrawal, Texas Instruments

"The Xilinx AI Engine: High Performance with Future-proof Architecture Adaptability"
Nick Ni, Xilinx

"A Self-service Platform to Deploy State-of-the-art Deep Learning Models in Under 30 Minutes"
Peter Zatloukal, Xnor.ai

"An Ultra-low-power Multi-core Engine for Inference on Encrypted DNNs"
Petronel Bigioi, Xperi

Fundamentals Track Presentations

"Training Data for Your CNN: What You Need and How to Get It"
Carlo Dal Mutto, Aquifi

"Fundamentals of Monocular SLAM"
Shrinivas Gadkari, Cadence

"How to Choose a 3D Vision Sensor"
Chris Osterwood, Capable Robot Components

"Selecting the Right Imager for Your Embedded Vision Application"
Chris Osterwood, Capable Robot Components

"Introduction to Optics for Embedded Vision"
Jessica Gehlhar, Edmund Optics (former)

"Deep Learning for Manufacturing Inspection Applications"
Stephen Se, FLIR Systems

"Creating Efficient, Flexible and Scalable Cloud Computer Vision Applications: An Introduction"
Nishita Sant and Greg Chu, GumGum

"Object Detection for Embedded Markets"
Paul Brasnett, Imagination Technologies

"Building Complete Embedded Vision Systems on Linux—From Camera to Display"
Clay D. Montgomery, Montgomery One

"Eye Tracking for the Future: The Eyes Have It"
Peter Milford, Parallel Rules

"Separable Convolutions for Efficient Implementation of CNNs and Other Vision Algorithms"
Chen-Ping Yu, Phiar

"Fundamental Security Challenges of Embedded Vision"
Mike Borza, Synopsys

"Five+ Techniques for Efficient Implementation of Neural Networks"
Bert Moons, Synopsys

"An Introduction to Machine Learning and How to Teach Machines to See"
Facundo Parodi, Tryolabs

Technical Insights Track Presentations

"Distance Estimation Solutions for ADAS and Automated Driving"
Gergely Debreczeni, AImotive

"Optimizing SSD Object Detection for Low-power Devices"
Moses Guttmann, Allegro

"Selecting and Exploiting Sensors for Sensor Fusion in Consumer Robots"
Daniel Casner, Anki (former)

"Performance Analysis for Optimizing Embedded Deep Learning Inference Software"
Gian Marco Iodice, Arm

"Sensory Fusion for Scalable Indoor Navigation"
Oleg Sinyavskiy, Brain Corp

"Portable Performance via the OpenVX Computer Vision Library: Case Studies"
Frank Brill, Cadence

"Machine Learning- based Image Compression: Ready for Prime Time?"
Michael Gormish, Clarifai

"Can We Have Both Safety and Performance in AI for Autonomous Vehicles?"
Andrew Richards, Codeplay Software

"Hardware-aware Deep Neural Network Design"
Peter Vajda, Facebook

"DNN Challenges and Approaches for L4/L5 Autonomous Vehicles"
Tom Wilson, Graphcore

"AI Reliability Against Adversarial Inputs"
Gokcen Cilingir and Li Chen, Intel

"Fast and Accurate RMNet: A New Neural Network for Embedded Vision"
Ilya Krylov, Intel

"Data Annotation at Scale: Pitfalls and Solutions"
Nikita Manovich, Intel

"Object Trackers: Approaches and Applications"
Minje Park, Intel

"APIs for Accelerating Vision and Inferencing: An Industry Overview of Options and Trade-offs"
Neil Trevett, Khronos Group and NVIDIA

"Can Simulation Solve the Training Data Problem?"
Peter McGuinness, Mindtech

"OpenCV: Current Status and Future Plans"
Satya Mallick, OpenCV.org

"Challenges and Approaches for Extracting Meaning from Satellite Imagery"
Adam Kraft, Orbital Insight

"Using Deep Learning for Video Event Detection on a Compute Budget"
Praveen Nayak, PathPartner Technology

"Low-power Computer Vision: Status, Challenges and Opportunities"
Yung-Hsiang Lu, Purdue University

"Designing Home Monitoring Cameras for Scale"
Ilya Brailovskiy and Changsoo Jeong, Ring

"Can Simulation Solve the Training Data Problem?"
Audrey Jill Boguchwal, Samasource

"Teaching Machines to See, Understand, Describe and Predict Sports Games in Real Time"
Mehrsan Javan, Sportlogiq

"Mining Site Data Extraction Using 3D Machine Learning"
Ravi Sahu, Strayos

"Enabling Automated Design of Computationally Efficient Deep Neural Networks"
Bichen Wu, University of California, Berkeley

"AI-powered Identity: Evaluating Face Recognition Capabilities"
Ioannis Kakadiaris, University of Houston

"Improving the Safety and Performance of Automated Vehicles Through Precision Localization"
Phil Magney, VSI Labs

"Methods for Creating Efficient Convolutional Neural Networks"
Mohammad Rastegari, Xnor.ai

"Beyond CNNs for Video: The Chicken vs. the Datacenter"
Steve Teig, Xperi

Technology Showcase Demonstrations

Cadence

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Cadence

Crossbar

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videantis

videantis

Xnor.ai

Xnor.ai

See you at the Summit! May 18-21 in Santa Clara, California!