Resources
In-depth information about the edge AI and vision applications, technologies, products, markets and trends.
The content in this section of the website comes from Edge AI and Vision Alliance members and other industry luminaries.
All Resources
NXP Accelerates the Transformation to Software-defined Vehicles (SDV) with Agreement to Acquire TTTech Auto
NXP strengthens its automotive business with a leading software solution provider specialized in the systems, safety and security required for SDVs TTTech Auto complements and accelerates the NXP CoreRide platform, enabling automakers to reduce complexity,
Optimizing Multimodal AI Inference
This blog post was originally published at Intel’s website. It is reprinted here with the permission of Intel. Multimodal models are becoming essential for AI, enabling the integration of diverse data types into a single
NVIDIA Blackwell GeForce RTX 50 Series Opens New World of AI Computer Graphics
Next Generation of GeForce RTX GPUs Deliver Stunning Visual Realism and 2x Performance Increase, Made Possible by AI, Neural Shaders and DLSS 4 January 6, 2025 — CES — NVIDIA today unveiled the most advanced
Qualcomm Brings Industry-leading AI Innovations and Broad Collaborations to CES 2025 Across PC, Automotive, Smart Home and Enterprises
Highlights: Spotlight on bringing edge AI across devices and computing spaces, including PC, automotive, smart home and into enterprises broadly, with global ecosystem partners at the show. In PC, continued traction for the Snapdragon X
Qualcomm Aware Unveils New Services to Drive Connected Intelligence Across Industries
Highlights: Qualcomm Aware adds observability, monitoring and location services to enable the development of IoT solutions that meet specific needs and challenges of consumers and enterprises across a wide range of industries and use cases.
Snapdragon X Series Continues to Redefine the PC Category with a New Platform, Mini Desktop Form Factors, and NPU Powered AI Experiences
Highlights: The 4th platform to join the Snapdragon X Series, Snapdragon X, brings AI PC leadership to Copilot+ PCs in the $600 range. Snapdragon X Series continues to gain traction with now over 60 designs
AMD Announces Expanded Consumer and Commercial AI PC Portfolio at CES
AMD Ryzen™ AI Max, AMD Ryzen™ AI 300 Series and AMD Ryzen™ 200 Series processors bring incredible performance for next-gen AI PCs AMD Ryzen™ AI Max PRO, AMD Ryzen™ AI 300 PRO and AMD Ryzen™
Qualcomm Launches On-prem AI Appliance Solution and Inference Suite to Step-up AI Inference Privacy, Flexibility and Cost Savings Across Enterprise and Industrial Verticals
Highlights: Qualcomm AI On-Prem Appliance Solution is designed for generative AI inference and computer vision workloads on dedicated on-premises hardware – allowing sensitive customer data, fine-tuned models, and inference loads to remain on premises. Qualcomm
Axelera AI at CES: Honored to Showcase Innovation at the Edge
Axelera AI is thrilled to join the global innovation of CES 2025, where the spotlight will shine on our cutting-edge Metis product line and its ability to redefine AI inference at the edge. Our fully
Technologies
The Automotive Radar Market: Three Key Takeaways
Front mounted short-range side radars enabling junction pedestrian automatic emergency braking will be a key source of automotive radar market growth. The automotive industry has been using radar for two and a half decades. During that time, it has transformed from enabling luxury features on the most expensive cars, to being used ubiquitously for basic
Improving Vision Model Performance Using Roboflow and Tenyks
This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. When improving an object detection model, many engineers focus solely on tweaking the model architecture and hyperparameters. However, the root cause of mediocre performance often lies in the data itself. In this collaborative post between Roboflow and
Federated Learning: Risks and Challenges
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. In the first article of our mini-series on Federated Learning (FL), Privacy-First AI: Exploring Federated Learning, we introduced the basic concepts behind the decentralized training approach, and we also presented potential applications in certain domains. Undoubtedly, FL
Applications
The Automotive Radar Market: Three Key Takeaways
Front mounted short-range side radars enabling junction pedestrian automatic emergency braking will be a key source of automotive radar market growth. The automotive industry has been using radar for two and a half decades. During that time, it has transformed from enabling luxury features on the most expensive cars, to being used ubiquitously for basic
Robotaxis on the Rise: Exploring Autonomous Vehicles
Robotaxis are proving they can offer driverless services in certain cities, as a means of accessible and modern public transport. IDTechEx states in its latest report, “Autonomous Vehicles Market 2025-2045: Robotaxis, Autonomous Cars, Sensors“, that testing is taking place worldwide, with the most commercial deployment happening in China currently. The report explores the commercial readiness
Edge Intelligence and Interoperability are the Key Components Driving the Next Chapter of the Smart Home
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. The smart home industry is on the brink of a significant leap forward, fueled by generative AI and edge capabilities The smart home is evolving to include advanced capabilities, such as digital assistants that interact like friends
Functions
The Automotive Radar Market: Three Key Takeaways
Front mounted short-range side radars enabling junction pedestrian automatic emergency braking will be a key source of automotive radar market growth. The automotive industry has been using radar for two and a half decades. During that time, it has transformed from enabling luxury features on the most expensive cars, to being used ubiquitously for basic
Improving Vision Model Performance Using Roboflow and Tenyks
This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. When improving an object detection model, many engineers focus solely on tweaking the model architecture and hyperparameters. However, the root cause of mediocre performance often lies in the data itself. In this collaborative post between Roboflow and
NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance (Part 3 of 3)
This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. During this series, we will use Tenyks to build a data-centric pipeline to debug and fix a model trained with the NVIDIA TAO Toolkit. Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline based
