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Full-Stack AI Systems Development with Murali Akula - #563

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内容由TWIML and Sam Charrington提供。所有播客内容(包括剧集、图形和播客描述)均由 TWIML and Sam Charrington 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Today we’re joined by Murali Akula, a Sr. director of Software Engineering at Qualcomm. In our conversation with Murali, we explore his role at Qualcomm, where he leads the corporate research team focused on the development and deployment of AI onto Snapdragon chips, their unique definition of “full stack”, and how that philosophy permeates into every step of the software development process. We explore the complexities that are unique to doing machine learning on resource constrained devices, some of the techniques that are being applied to get complex models working on mobile devices, and the process for taking these models from research into real-world applications. We also discuss a few more tools and recent developments, including DONNA for neural architecture search, X-Distill, a method of improving the self-supervised training of monocular depth, and the AI Model Effeciency Toolkit, a library that provides advanced quantization and compression techniques for trained neural network models.

The complete show notes for this episode can be found at twimlai.com/go/563

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Manage episode 322709737 series 2355587
内容由TWIML and Sam Charrington提供。所有播客内容(包括剧集、图形和播客描述)均由 TWIML and Sam Charrington 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Today we’re joined by Murali Akula, a Sr. director of Software Engineering at Qualcomm. In our conversation with Murali, we explore his role at Qualcomm, where he leads the corporate research team focused on the development and deployment of AI onto Snapdragon chips, their unique definition of “full stack”, and how that philosophy permeates into every step of the software development process. We explore the complexities that are unique to doing machine learning on resource constrained devices, some of the techniques that are being applied to get complex models working on mobile devices, and the process for taking these models from research into real-world applications. We also discuss a few more tools and recent developments, including DONNA for neural architecture search, X-Distill, a method of improving the self-supervised training of monocular depth, and the AI Model Effeciency Toolkit, a library that provides advanced quantization and compression techniques for trained neural network models.

The complete show notes for this episode can be found at twimlai.com/go/563

  continue reading

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