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Edge AI and Vision Insights: July 6, 2022 Edition

EDGE AI DEVELOPMENT AND DEPLOYMENT Deploying PyTorch Models for Real-time Inference On the Edge In this 2021 Embedded Vision Summit presentation, Moritz August, CDO at Nomitri GmbH, provides an overview of workflows for deploying compressed deep learning models, starting with PyTorch and creating native C++ application code running in real-time on embedded hardware platforms. He …

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“A Platform Approach to Developing Networked Visual AI Systems,” a Presentation from Network Optix

Nathan Wheeler, Chairman and CEO, and Tony Luce, Vice President of Product Marketing, both of Network Optix, present the “Platform Approach to Developing Networked Visual AI Systems” tutorial at the May 2022 Embedded Vision Summit. Connected cameras are becoming ubiquitous. Coupled with CV and ML, they enable a growing range… “A Platform Approach to Developing …

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Neural Network Optimization with AIMET

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. To run neural networks efficiently at the edge on mobile, IoT, and other embedded devices, developers strive to optimize their machine learning (ML) models’ size and complexity while taking advantage of hardware acceleration for inference. For these …

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“Enable Spatial Understanding for Embedded/Edge Devices with DepthAI,” a Presentation from Luxonis

Erik Kokalj, Director of Applications Engineering at Luxonis, presents the “Enable Spatial Understanding for Embedded/Edge Devices with DepthAI” tutorial at the May 2022 Embedded Vision Summit. Many systems need to understand not only what objects are nearby, but also where those objects are in the physical world. This is “spatial… “Enable Spatial Understanding for Embedded/Edge …

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BrainChip Partners with Prophesee Optimizing Computer Vision AI Performance and Efficiency

Laguna Hills, Calif. – June 14, 2022 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of neuromorphic AI IP, and Prophesee, the inventor of the world’s most advanced neuromorphic vision systems, today announced a technology partnership that delivers next-generation platforms for OEMs looking to integrate event-based vision systems …

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“Accelerate Tomorrow’s Models with Lattice FPGAs,” a Presentation from Lattice Semiconductor

Hussein Osman, Segment Marketing Director at Lattice Semiconductor, presents the “Accelerate Tomorrow’s Models with Lattice FPGAs” tutorial at the May 2022 Embedded Vision Summit. Deep learning models are advancing at a dizzying pace, creating difficult dilemmas for system developers. When you begin developing an edge AI system, you select the… “Accelerate Tomorrow’s Models with Lattice …

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The LEC-2290: An Edge AI Appliance for Traffic Infraction Prevention

This blog post was originally published at Lanner Electronics’ website. It is reprinted here with the permission of Lanner Electronics. In dense urban environments where traffic flow is heavy, keeping up with traffic control and maintaining road safety at intersections with the heaviest traffic is often tricky as vehicles and pedestrians’ behaviors are erratic and …

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“Taking Intelligent Video Analytics to the Next Level,” a Presentation from Hailo

Avi Baum, CTO and Co-founder of Hailo, presents the “Taking Intelligent Video Analytics to the Next Level,” tutorial at the May 2022 Embedded Vision Summit. In this presentation, Baum examines how powerful edge AI improves the value proposition of video analytics across market segments such as physical security, access control,… “Taking Intelligent Video Analytics to …

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e-con Systems Launches Multi-camera Solution for NVIDIA Jetson AGX Orin Based on Sony STARVIS IMX485

NVIDIA Jetson AGX Orin | Sony STARVIS IMX485 | 1/1.2” sensor | Large pixel size | 4K resolution | Multi-camera solution San Jose and Chennai (June 21, 2022) – e-con Systems, a leading embedded OEM camera company and an NVIDIA Elite Partner, today launched the e-CAM82_CUOAGX – a 4K ultra-low-light camera powered by the NVIDIA …

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Modeling for the Edge: How Neural Network Modelers Should Evaluate Edge Compute Power

This blog post was originally published at Syntiant’s website. It is reprinted here with the permission of Syntiant. Academic machine learning requires extensive toolsets for model training and evaluation, but the vast majority of academic work is more grounded in model capacity than looking at the energy cost of deploying those models. This puts modelers …

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