Software

Harnessing the Power of LLM Models on Arm CPUs for Edge Devices

This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. In recent years, the field of machine learning has witnessed significant advancements, particularly with the development of Large Language Models (LLMs) and image generation models. Traditionally, these models have relied on powerful cloud-based infrastructures to deliver impressive […]

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AI On the Road: Why AI-powered Cars are the Future

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. AI transforms your driving experience in unexpected ways as showcased by Qualcomm Technologies collaborations As automotive technology rapidly advances, consumers are looking for vehicles that deliver AI-enhanced experiences through conversational voice assistants and sophisticated user interfaces. Automotive

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CPUs, GPUs, and AI: Exploring High-performance Computing Hardware

IDTechEx‘s latest report, “Hardware for HPC and AI 2025-2035: Technologies, Markets, Forecasts“, provides data to show that the use of graphics processing units (GPUs) within high performance computing (HPC) has been increasingly adopted since the introduction of generative AI and large language models, as well as advanced memory, storage, networking, and cooling technologies. Drivers for

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Visual Intelligence at the Edge

This blog post was originally published at Au-Zone Technologies’ website. It is reprinted here with the permission of Au-Zone Technologies. Optimizing AI-based video telematics deployments on constrained SoCs platforms The demand for advanced video telematics systems is growing rapidly as companies seek to enhance road safety, improve operational efficiency, and manage liability costs with AI-powered

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NVIDIA JetPack 6.2 Brings Super Mode to NVIDIA Jetson Orin Nano and Jetson Orin NX Modules

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. The introduction of the NVIDIA Jetson Orin Nano Super Developer Kit sparked a new age of generative AI for small edge devices. The new Super Mode delivered an unprecedented generative AI performance boost of up to 1.7x

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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

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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

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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

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Accelerate Custom Video Foundation Model Pipelines with New NVIDIA NeMo Framework Capabilities

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Generative AI has evolved from text-based models to multimodal models, with a recent expansion into video, opening up new potential uses across various industries. Video models can create new experiences for users or simulate scenarios for training

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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

NVIDIA TAO Toolkit: How to Build a Data-centric Pipeline to Improve Model Performance  (Part 3 of 3) Read More +

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.

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