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September 2020 Embedded Vision Summit Replay

Keynote and General Session

“Streamline, Simplify, and Solve for the Edge of the Future,” a General Session Presentation from Intel

“A New Golden Age for Computer Architecture: Processor Innovation to Enable Ubiquitous AI,” a Keynote Presentation from David Patterson

“Perspective On the Past, Present, and Future of Processor Design,” an Alliance Interview with David Patterson

Overview

September 2020 Embedded Vision Summit Opening Remarks (September 15)

September 2020 Embedded Vision Summit Opening Remarks (September 17)

September 2020 Embedded Vision Summit Opening Remarks (September 22)

September 2020 Embedded Vision Summit Opening Remarks (September 24)

September 2020 Embedded Vision Summit Slides

Business Insights

“Market Trends in Automotive Perception: From Insect-Like to Human-Like Intelligence,” a Presentation from Yole Développement

“Opportunities for Vision in Healthcare,” a Presentation from Woodside Capital

“Embedded Vision: Where’s The Money?,” a Presentation from Woodside Capital

“Embedded Vision in ADAS and Autonomous Vehicles: Navigating the New Reality,” a Presentation from Strategy Analytics

“Using Learning at the Edge to Deliver Business Value,” a Presentation from LG Electronics

“How 5G is Pushing Processing to the Edge,” a Presentation from Inseego

“Lessons From the Start-up Trenches,” a Presentation by and Interview with Oliver Gunasekara

“Can You Patent Your AI-Based Invention?,” a Presentation from Fitch, Even, Tabin & Flannery LLP

“Key Trends and Challenges in Practical Visual AI and Computer Vision,” a Presentation from the Edge AI and Vision Alliance

“Machine Learning for the Real World: What is Acceptable Accuracy, and How Can You Achieve It?,” a Presentation from Arm

“The Opportunities and Challenges in Realizing the Potential for AI at the Edge: An Update from the Front Lines,” An Embedded Vision Summit Expert Panel Discussion

Enabling Technologies

“Driver Monitoring Systems: Present and Future,” a Presentation from XPERI

“High-Bandwidth Multicamera Systems with PCIe Backbone: Snapshot and Outlook on Technologies and Applications,” a Presentation from XIMEA

“Vitis and Vitis AI: Application Acceleration from Cloud to Edge,” a Presentation from Xilinx

“Making Edge AI Inference Programming Easier and Flexible,” a Presentation from Texas Instruments

“OpenCV: Rapid Growth and Evolution Beyond the Library,” a Presentation from OpenCV

“Deploying Deep Learning Applications on FPGAs with MATLAB,” a Presentation from MathWorks

“How to Create Your Own AI-Enabled Camera Solution in Days,” a Presentation from IDS Imaging Development Systems

“Lessons Learned from the Deployment of Deep Learning Applications In Edge Devices,” a Presentation from Hailo

“Memory Allocation in AI and Computer Vision Applications,” a Presentation from CEVA

“Designing Cameras to Detect the “Invisible”: Handling Edge Cases Without Supervision,” a Presentation from Algolux

“Enabling Embedded AI for Healthcare,” a Presentation from VeriSilicon

“Advancing Embedded Vision for an Autonomous World,” a Presentation from Qualcomm

“Benchmarking vs. Benchmarketing: Why Should You Care?,” a Presentation from Qualcomm

“Qualcomm AI Leading the Way with Distributed Intelligence,” a Presentation from Qualcomm

“Ergo: Perceive’s Chip – Data Center-Class Inference in Edge Devices at Ultra-Low Power,” a Presentation from Perceive

“Machine-Learning-Based Perception on a Tiny, Low-Power FPGA,” a Presentation from Lattice Semiconductor

“Cadence Tensilica Edge AI Processor IP Solutions for Broad Market Use Cases,” a Presentation from Cadence

“Smarter Manufacturing with Intel’s Deep Learning-Based Machine Vision,” a Presentation from Intel

“Acceleration of Deep Learning Using OpenVINO: 3D Seismic Case Study,” a Presentation from Intel

“Federated Edge Computing System Architectures,” a Presentation from Intel

“Edge Inferencing—Scalability with Intel Vision Accelerator Design Cards,” a Presentation from Intel

“Getting Efficient DNN Inference Performance: Is It Really About the TOPS?,” a Presentation from Intel

Over the Shoulder

“Optimize, Deploy and Scale Edge AI and Video Analytics Applications,” a Presentation from NVIDIA and Amazon

“Accelerating Time to an Image Processing Prototype,” a Presentation from Avnet

“AI-powered People Detection Using Time of Flight Data,” a Presentation from Arrow Electronics and Analog Devices

“Smart Factory and Smart Life: AI Embedded Vision Camera,” a Presentation from LAON PEOPLE

Fundamentals

“Practical Guide to Implementing Deep Neural Network Inferencing at the Edge,” a Presentation from Zebra Technologies

“Introducing Machine Learning and How to Teach Machines to See,” a Presentation from Tryolabs

“Can You See What I See? The Power of Deep Learning,” a Presentation from StreamLogic

“Introduction to Simultaneous Localization and Mapping (SLAM),” a Presentation from Skydio

“Eye Tracking for the Future,” a Presentation from Parallel Rules

“Reinforcement Learning: a Practical Introduction,” a Presentation from Microsoft

“CMOS Image Sensors: A Guide to Building the Eyes of a Vision System,” a Presentation from GoPro

“Deep Learning on Mobile Devices,” a Presentation from Siddha Ganju

“Practical Image Data Augmentation Methods for Training Deep Learning Object Detection Models,” a Presentation from EJ Technology Consultants

“Deploying AI Software to Embedded Devices Using Open Standards,” a Presentation from Codeplay Software

“Trends in Neural Network Topologies for Vision at the Edge,” a Presentation from Synopsys

Technical Insights

“A Computer Vision-Based Personal Trainer That Runs On Your Phone,” a Presentation from Twenty BN

“Modern SoCs for Consumer Robotics and AIoT,” a Presentation from Trifo

“Designing Bespoke CNNs for Target Hardware,” a Presentation from StradVision

“Tackling Extreme Visual Conditions for Autonomous UAVs In the Wild,” a Presentation from Skydio

“Multi-modal Re-identification: IOT + Computer Vision for Residential Community Tracking,” a Presentation from Seedland

“New Methods for Implementation of 2-D Convolution for Convolutional Neural Networks,” a Presentation from Santa Clara University

“Improving Power Efficiency for Edge Inferencing with Memory Management Optimizations,” a Presentation from Samsung

“Image-Based Deep Learning for Manufacturing Fault Condition Detection,” a Presentation from Samsung

“Using an ISP for Real-time Data Augmentation,” a Presentation from Pony.AI

“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Presentation from the University of Washington

“Imaging Systems for Applied Reinforcement Learning Control,” a Presentation from Nanotronics

“MLPerf: An Industry Standard Performance Benchmark Suite for Machine Learning,” a Presentation from Facebook and Arizona State University

“Khronos Standard APIs for Accelerating Vision and Inferencing,” a Presentation from the Khronos Group

“Feeding the World Through Embedded Vision,” a Presentation from John Deere

“Challenges and Approaches for Cascaded DNNs: A Case Study of Face Detection for Face Verification,” a Presentation from Imagination Technologies

“Sea-thru: A Method for Removing Water from Underwater Images,” a Presentation from the Harbor Branch Oceanographic Institute

“Safer and More Efficient Intersections with Computer Vision,” a Presentation from Cubic | GRIDSMART

“Deep Learning for Manufacturing Inspection: Case Studies,” a Presentation from FLIR Systems

“Practical DNN Quantization Techniques and Tools,” a Presentation from Facebook

“Video Activity Recognition with Limited Data for Smart Home Applications,” a Presentation from Comcast

“Combining CNNs and Conventional Algorithms for Low-Compute Vision: A Case Study in the Garage,” a Presentation from the Chamberlain Group

“Structures as Sensors: Smaller-Data Learning in the Physical World,” a Presentation from Carnegie Mellon University

“Joint Regularization of Activations and Weights for Efficient Neural Network Pruning,” a Presentation from Black Sesame Technologies

“Parallelizing Machine Learning Applications in the Cloud with Kubernetes: A Case Study,” a Presentation from AMD

“Democratizing Computer Vision and Machine Learning with Open, Royalty-Free Standards: OpenVX,” a Presentation from AMD

“Accuracy: Beware of Red Herrings and Black Swans,” a Presentation from Perceive

“Recent Advances in Post-training Quantization,” a Presentation from Intel

“The Future of Image Sensors,” An Embedded Vision Summit Expert Panel Discussion

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