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Partitioning Strategies to Optimize AI Inference for Multi-core Platforms

This blog post was originally published at Ceva’s website. It is reprinted here with the permission of Ceva. Not so long ago, AI inference at the edge was a novelty easily supported by a single NPU IP accelerator embedded in the edge device. Expectations have accelerated rapidly since then. Now we want embedded AI inference […]

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FRAMOS to Showcase Automation Solutions at the Largest Robotics and Automation Show in North America

15th of April 2024 – FRAMOS, a leading global expert in vision systems dedicated to innovation and excellence in enabling devices to see and think, is pleased to announce its presence at the Automate Show 2024 in Chicago, Illinois, USA. FRAMOS will be joining more than 800 game-changing exhibitors at the Automate Show from May

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Exploring the Impact of Generative AI: Identifying the Winners

This market research report was originally published at the Yole Group’s website. It is reprinted here with the permission of the Yole Group. Datacenter GPU and AI ASIC revenue could reach $156 billion by 2025 and $233 billion by 2029. OUTLINE The massive growth that the data center GPU and AI ASIC market experienced in

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Efficiently Packing Neural Network AI Model for the Edge

This blog post was originally published at Ceva’s website. It is reprinted here with the permission of Ceva. Packing applications into constrained on-chip memory is a familiar problem in embedded design, and is now equally important in compacting neural network AI models into a constrained storage. In some ways this problem is even more challenging

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AI Decoded From GTC: The Latest Developer Tools and Apps Accelerating AI on PC and Workstation

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Next Chat with RTX features showcased, TensorRT-LLM ecosystem grows, AI Workbench general availability, and NVIDIA NIM microservices launched. Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more

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The Rise of Generative AI: A Timeline of Breakthrough Innovations

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Explore the most pivotal advancements that shaped the landscape of generative AI From Alan Turing’s pioneering work to the cutting-edge transformers of the present, the field of generative artificial intelligence (AI) has witnessed remarkable breakthroughs — and

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Micron’s Full Suite of Automotive-grade Solutions Qualified for Qualcomm Automotive Platforms to Power AI in Vehicles

Micron’s automotive memory and storage enable central compute, digital cockpit and advanced driver-assistance systems for Qualcomm customers NUREMBERG, Germany, April 10, 2024 (GLOBE NEWSWIRE) — Embedded World — Micron Technology, Inc. (Nasdaq: MU), today announced that it has qualified a full suite of its automotive-grade memory and storage solutions for Qualcomm Technologies Inc.’s Snapdragon® Digital Chassis™,

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From Challenger to Leader: China Consolidates Its Presence in Thermal Imaging

This market research report was originally published at the Yole Group’s website. It is reprinted here with the permission of the Yole Group. China shifts its focus to thermal imaging technologies, securing 50% of worldwide shipments in 2023. OUTLINE The thermal camera market is expected to grow to US$9.1 billion in 2029, with an estimated

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Advantech Establishes Collaboration with Qualcomm to Shape the Future of the Edge

Taipei, Taiwan and Nuremberg, Germany, 10th April 2024 — Today, at Embedded World, Advantech proudly announced its strategic collaboration with Qualcomm Technologies, Inc. to revolutionize the edge computing landscape. This effort, combining AI expertise, high-performance computing, and industry-leading connectivity, is set to propel innovation for industrial computing. This collaboration establishes an open and diverse edge

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Axelera Uses oneAPI Construction Kit to Rapidly Enable Open Standards Programming for the Metis AIPU

AI applications have an endless hunger for computational power. Currently, increasing the sizes of the models and cranking up the number of parameters has not quite yet reached the point of diminishing returns and thus the ever growing models still yield better performance than their predecessors. At the same time, new areas for application of

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