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Intel Announces New Edge Platform for Scaling AI Applications

Algorithms, Intel, News, Processors, Software, Tools /

The edge-native software platform simplifies development, deployment and management of edge AI applications. What’s New: At MWC 2024, Intel announced its new Edge Platform, a modular, open software platform enabling enterprises to develop, deploy, run, secure, and manage edge and AI applications at scale with cloud-like simplicity. Together, these capabilities will accelerate time-to-scale deployment for […]

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Edge AI in Medical Applications

Algorithms, Blog Posts, Digica, Medical, Object Identification, Software, Tools /

This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. A practical use for object detection based on Convolutional Neural Networks is in devices which can support people with impaired vision. An embedded device which runs object-detection models can make everyday life easier for users with such

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Qualcomm Continues to Bring the Generative AI Revolution to Devices and Empowers Developers with Qualcomm AI Hub

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Highlights: Qualcomm now enables at-scale on-device AI commercialization across next-generation PCs, smartphones, software-defined vehicles, XR devices, IoT and more – bringing intelligent computing everywhere. Qualcomm AI Hub offers 75+ optimized AI models for Snapdragon and Qualcomm platforms, reducing time-to-market for developers and unlocking the benefits of on-device AI for their apps. These models are also

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Optimizing Generative AI for Edge Devices

Algorithms, Blog Posts, Processors, Qualcomm, Software, Tools /

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. How pruning, quantization and knowledge distillation help fit generative AI into resource-limited edge devices As of April 2023, one in three U.S. adults have used generative artificial intelligence (AI): Are you among them?1 When OpenAI unveiled ChatGPT

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Shining Brighter Together: Google’s Gemma Optimized to Run on NVIDIA GPUs

Algorithms, News, NVIDIA, Processors, Software, Tools /

New open language models from Google accelerated by TensorRT-LLM across NVIDIA AI platforms — including local RTX AI PCs. NVIDIA, in collaboration with Google, today launched optimizations across all NVIDIA AI platforms for Gemma — Google’s state-of-the-art new lightweight 2 billion– and 7 billion-parameter open language models that can be run anywhere, reducing costs and

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An In-depth Comparison of LiDAR, Camera and Radar Technologies

Algorithms, Blog Posts, Object Identification, Object Tracking, Outsight, Software, Tools /

This blog post was originally published at Outsight’s website. It is reprinted here with the permission of Outsight. This article explores the capabilities and limitations of each type of sensor, to provide a clear understanding of why LiDAR has emerged as a strong contender in the computer vision tech race. This article delves into the

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IoT for Dummies: Decoding the Language of Your Smart Building

Algorithms, Blog Posts, Digica, Security, Sensors and Cameras, Software, Tools /

This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. As smart home technology continues to evolve, our homes are becoming equipped with an increasing number of sensors, each capable of generating valuable data. From security systems and motion detectors to monitoring electricity and water usage, these

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Benchmarking Camera Performance on Your Workstation with NVIDIA Isaac Sim

Algorithms, Blog Posts, NVIDIA, Processors, Robotics, Software, Tools /

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Robots are typically equipped with cameras. When designing a digital twin simulation, it’s important to replicate its performance in a simulated environment accurately. However, to make sure the simulation runs smoothly, it’s crucial to check the performance

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Quantization of Convolutional Neural Networks: Model Quantization

Algorithms, Articles, Software, Tools /

See “From Theory to Practice: Quantizing Convolutional Neural Networks for Practical Deployment” for the previous article in this series. Significant progress in Convolutional Neural Networks (CNNs) has focused on enhancing model complexity while managing computational demands. Key advancements include efficient architectures like MobileNet1, SqueezeNet2, ShuffleNet3, and DenseNet4, which prioritize compute and memory efficiency. Further innovations

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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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