This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. How our pioneering research and leading proof-of-concepts are paving the way for generative AI to scale What you should know: Qualcomm AI Research is pioneering research and inventing novel techniques to deliver efficient, high-performance GenAI solutions. Our […]
GenAI Firsts: Redefining What’s Possible At the Edge Read More +
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. The default Python V4L2 library module contains critical details related to the V4L2 capture method. Learn how to implement basic definitions (missing the default library module) and capture images in the V4L2 multi-planar format. Python
How to Support Multi-planar Format in Python V4L2 Applications on i.MX8M Plus Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. AI and OpenUSD accelerate safe, scalable autonomous vehicle development by enabling simulation-first approaches. Editor’s note: This blog is a part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their
This blog post was originally published at SiMa.ai’s website. It is reprinted here with the permission of SiMa.ai. LLiMa represents a paradigm shift in physical AI deployment that fundamentally changes how enterprises approach GenAI integration, enabling real Physical AI. While competitors typically offer pre-optimized models that were manually tuned for specific hardware configurations, LLiMa takes
LLiMa: Real-time Edge Generative AI Under 10W, Built for You Read More +
This blog post was originally published at Geisel Software’s Symage website. It is reprinted here with the permission of Geisel Software. Synthetic data is becoming increasingly integral to AI and analytics, with many projects now incorporating these datasets. While synthetic data generated using generative AI techniques offers valuable insights, simulation-based synthetic datasets enhance this process
How Synthetic Datasets are Revolutionizing AI Training Across Industries Read More +
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. AI device powered by Qualcomm Dragonwing boosts productivity and reduces cloud dependence in Capgemini’s monitoring application for grade crossings Capgemini moved from their previous hardware solution to an edge AI device powered by the Qualcomm® Dragonwing™ QCS6490
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. Mobility systems require front-view cameras that depend on visual intelligence. Hence, the camera’s input is the starting point for every decision. Learn why 4K HDR imaging is critical in front-view cameras and explore five major
Why 4K HDR Imaging is Required in Front View Cameras? Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. At the conference in Palo Alto, California, NVIDIA experts detail how NVIDIA NVLink and Spectrum-X Ethernet technologies, Blackwell and CUDA accelerate inference for millions of AI workflows across the globe. AI reasoning, inference and networking will be
This blog post was originally published at SiMa.ai’s website. It is reprinted here with the permission of SiMa.ai. Generative AI has ushered in a new era of technological progress, reminiscent of the rise of the internet in the 1990s. Beyond the impressive chatbots we’re now used to, the constant flow of innovation has introduced new
AI at the Edge: The Next Gold Rush Read More +
This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. In the rapidly evolving field of artificial intelligence, edge computing has become increasingly vital for deploying intelligent systems in real-world environments where power, latency, and bandwidth are limited: we need neural network models to run efficiently. For
Akida Exploits Sparsity For Low Power in Neural Networks Read More +
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