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Now available—the Embedded Vision Summit On-Demand Edition! Gain valuable computer vision and edge AI insights and know-how from the experts at the 2021 Summit.

Enabling Technologies

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Technical Insights

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

Contact

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1646 North California Blvd.,
Suite 360
Walnut Creek, CA 94596 USA

Phone
Phone: +1 (925) 954-1411
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