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OmniVision Automotive Image Sensors Compatible with NVIDIA DRIVE Platform, Including Driver Monitoring Imagers for NVIDIA DRIVE Hyperion - News
April 19, 2021OmniVision announced at the NVIDIA GPU Technology Conference (GTC) that it has officially joined the NVIDIA DRIVE autonomous vehicle development ecosystem
Industrial
Boost AI Capabilities at the Edge Using AIM - Video
April 09, 2021The Aetina Intelligent Management (AIM) framework simplifies the design and deployment of an AI platform that’s destined to reside at the Edge of the industrial IoT (IIoT). Based on NVIDIA Jetson GPU, AIM can handle the compute-intensive demands that are sure to come.
IoT
Perform AI at the Edge with NGC-ready Hardware - Video
April 09, 2021A key technology behind AI at the Edge is the GPU. Harnessing the complexity of the GPU for applications like inferencing is not something that’s easily digestible for the novice engineer. That’s where folks like Lanner come into play. With the company’s Edge AI Appliance LEC-2290 system, engineers can focus on the specific application without having to deal with the underlying hardware.
Neousys Technology Releases the Nuvo-8108GC-XL Edge AI GPU Computing Platform - News
March 29, 2021Neousys Technology announced the release of its Nuvo-8108GC-XL, an edge AI GPU computing platform supporting the NVIDIA RTX 30 series GPU card up to RTX 3080 and Intel Xeon E, 9th/ 8th-Gen Core™ processors.
Maximize NVIDIA Technology When Deploying AI at the Edge - Blog
March 29, 2021The race to develop Edge AI servers is on, and it’s a hotly contested race.
Automate Your Logistics Operations Using AMRs to Increase Productivity and Efficiency - Blog
March 26, 2021The e-commerce and online retail industries are experiencing exponential growth, resulting in more stringent demands being placed on logistics operations worldwide. Logistics companies and their suppliers are relying on automation more than ever to increase productivity and efficiency.
Embedded AI: Out of the Lab and into the Field - Podcast
March 26, 2021How do we transition from our historical lack of understanding about what’s going on under the hood of complex neural networks, and into an era of AI explainability around how these models operate?