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Revolutionizing Edge Devices with Energy-Efficient Generative AI Techniques

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コンテンツは EDGE AI FOUNDATION によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、EDGE AI FOUNDATION またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

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Unlock the secrets of energy-efficient AI as we explore the groundbreaking fusion of Edge AI and generative AI in our latest episode. With expert insights from Victor Jung, a trailblazer in the field, discover how foundational models can be deployed on tiny and embedded systems to revolutionize devices like AR glasses and nanodrones. Listen as we unravel the complexities of deploying neural networks on microcontrollers, with a focus on powerful techniques like quantization, graph lowering, and innovative memory management strategies.
Victor guides us through the nuanced process of deploying neural networks, highlighting critical stages like graph lowering and memory allocation. Traverse the intricate front-end and mid-end stages where neural network graphs are optimized, ensuring peak performance on specific hardware platforms. We'll illustrate the importance of efficient memory usage through a fascinating example involving a tiny language model on the Syracuse platform, showcasing the role of quantization and memory management tailored for hardware constraints.
Dive into the future of AI deployment on edge devices with a focus on quantization and hardware support. From exploring the potential of foundation models like DenoV2 to discussing the emerging micro scaling format, we uncover the technologies that are making AI more energy-efficient and versatile. Our conversation underscores the importance of viewing memory as a compute asset and the need for ongoing research to enhance system efficiency for generative AI at the edge. Join us for an enlightening episode that highlights the vital steps needed to optimize memory and computing resources for meaningful applications on small platforms.

Support the show

Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

  continue reading

1. Revolutionizing Edge Devices with Energy-Efficient Generative AI Techniques (00:00:00)

2. Energy-Efficient Generative AI Deployment (00:00:36)

3. Deploying Graph Lowering and Memory Management (00:15:22)

4. Managing ONNX Graphs for ML Deployment (00:30:23)

5. Optimizing Edge Generative AI Deployment (00:40:59)

23 つのエピソード

Artwork
iconシェア
 
Manage episode 460214229 series 3574631
コンテンツは EDGE AI FOUNDATION によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、EDGE AI FOUNDATION またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Send us a text

Unlock the secrets of energy-efficient AI as we explore the groundbreaking fusion of Edge AI and generative AI in our latest episode. With expert insights from Victor Jung, a trailblazer in the field, discover how foundational models can be deployed on tiny and embedded systems to revolutionize devices like AR glasses and nanodrones. Listen as we unravel the complexities of deploying neural networks on microcontrollers, with a focus on powerful techniques like quantization, graph lowering, and innovative memory management strategies.
Victor guides us through the nuanced process of deploying neural networks, highlighting critical stages like graph lowering and memory allocation. Traverse the intricate front-end and mid-end stages where neural network graphs are optimized, ensuring peak performance on specific hardware platforms. We'll illustrate the importance of efficient memory usage through a fascinating example involving a tiny language model on the Syracuse platform, showcasing the role of quantization and memory management tailored for hardware constraints.
Dive into the future of AI deployment on edge devices with a focus on quantization and hardware support. From exploring the potential of foundation models like DenoV2 to discussing the emerging micro scaling format, we uncover the technologies that are making AI more energy-efficient and versatile. Our conversation underscores the importance of viewing memory as a compute asset and the need for ongoing research to enhance system efficiency for generative AI at the edge. Join us for an enlightening episode that highlights the vital steps needed to optimize memory and computing resources for meaningful applications on small platforms.

Support the show

Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

  continue reading

1. Revolutionizing Edge Devices with Energy-Efficient Generative AI Techniques (00:00:00)

2. Energy-Efficient Generative AI Deployment (00:00:36)

3. Deploying Graph Lowering and Memory Management (00:15:22)

4. Managing ONNX Graphs for ML Deployment (00:30:23)

5. Optimizing Edge Generative AI Deployment (00:40:59)

23 つのエピソード

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