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Discover how the EDGE AI FOUNDATION is evolving from its roots in the tinyML Foundation to becoming a central hub for innovation and collaboration. Learn how initiatives like EDGE AI Labs and the EDGE AIP program are bridging the gap between academia and industry, training future AI leaders while tackling the ethical challenges of responsible AI development.

Explore the transformative potential of generative AI on edge devices, from providing vital healthcare diagnostics in remote areas to enabling adaptive robotics in factories. We'll highlight compelling reasons for companies to engage with the Edge AI Foundation, offering unparalleled access to cutting-edge research, top talent, and a voice in shaping the industry's future. As we navigate through real-life scenarios and ethical considerations, you’ll see why the urgency and opportunity surrounding Edge AI is something you don't want to miss.

Join us on this journey to ensure the benefits of AI are shared widely and responsibly by visiting edgeaifoundation.org.

Learn more about the EDEG AI FOUNDATION - edgeaifoundation.org

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Unlock the secrets to deploying machine learning models on edge devices with Chen Lai from the PyTorch Edge team at Meta. Discover how XTorch, a brainchild of the PyTorch team, is transforming edge deployment by addressing challenges like memory constraints and hardware diversity. Get an insider's view on the technical collaborations with tech giants like Apple, Arm, Qualcomm, and MediaTek, which are revolutionizing the deployment of advanced language models like LLAMA on platforms such as iOS and Android. With Chen's expert insights, explore the fascinating process of converting PyTorch models into executable programs optimized for performance, stability, and broad hardware compatibility, ensuring seamless integration from server to edge environments.

Immerse yourself in the world of XTorch within the Red Bull Ecosystem, where deploying machine learning models becomes effortless even without extensive hardware knowledge. Learn how key components like Torchexport and Torchio capture compute graphs and support quantization, elevating edge deployment capabilities. Discover how Torchchat facilitates large language model inference on various devices, ensuring compatibility with popular models from Hugging Face. As we wrap up, hear about the community impact of Meta's Executorch initiative, showcasing a commitment to innovation and collaboration. Chen shares his passion and dedication to advancing edge computing, leaving a lasting impression on listeners eager for the next wave of technological breakthroughs.

Learn more about the tinyML Foundation - tinyml.org

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Unlock the future of TinyML by learning how to harness the power of large language models, as we sit down with Roberto Morabito to dissect this intriguing technological convergence. Discover how the collaborative efforts with Eurocom and the University of Helsinki are shaping a groundbreaking framework designed to elevate TinyML's lifecycle management. We promise to unravel the complexities and opportunities that stem from integrating these technologies, focusing on the essential role of prompt templates and the dynamic challenges posed by hardware constraints. Through a proof-of-concept demonstration, we bring you invaluable insights into resource consumption, potential bottlenecks, and the exciting prospect of automating lifecycle stages.

Our conversation ventures into optimizing language models for end devices, delving into the transformative potential of Arduinos and single-board computers in enhancing efficiency and slashing costs. Roberto shares his expertise on the nuances of model conversion across varying hardware capabilities, revealing the impact this has on success rates. The episode crescendos with a compelling discussion on automating industrial time series forecasting, underscoring the critical need for adaptive solutions to maintain accuracy and efficiency. Through Roberto's expert insights, listeners are invited to explore the forefront of technology that is poised to revolutionize industrial applications.

Learn more about the tinyML Foundation - tinyml.org