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Revolutionizing Edge Devices with Energy-Efficient Generative AI Techniques

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Manage episode 460214229 series 3574631
Innhold levert av EDGE AI FOUNDATION. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av EDGE AI FOUNDATION eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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

Kapitler

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)

24 episoder

Artwork
iconDel
 
Manage episode 460214229 series 3574631
Innhold levert av EDGE AI FOUNDATION. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av EDGE AI FOUNDATION eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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

Kapitler

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)

24 episoder

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