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Efficient Deployment of Models at the Edge // Krishna Sridhar // #284

Jan 17, 2025
In this engaging discussion, Krishna Sridhar, an engineering leader at Qualcomm and former co-founder of Tetra AI, dives into the efficient deployment of AI models at the edge. He shares insights on using Qualcomm AI Hub to optimize models for on-device performance, highlighting its application in real-time sports tracking and mobile photography. Krishna also explores the balance between hardware and software optimization in modern devices. Plus, he reveals how innovations in edge computing are transforming everyday AI applications while ensuring user privacy.
51:33

Episode guests

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Quick takeaways

  • Qualcomm's AI Hub simplifies model deployment on edge devices, enabling developers to efficiently optimize and run machine learning applications.
  • Real-time applications like AI-enhanced cricket tracking illustrate how edge computing empowers advanced analytics for everyday users with cost-effective solutions.

Deep dives

Innovations in AI on the Edge

Qualcomm is focusing on enhancing AI deployment on the edge by simplifying the process for developers. Their Qualcomm AI Hub streamlines how machine learning models can be brought into the platform and efficiently run on Qualcomm chips, significantly reducing complexity. The platform allows developers to upload their trained models, identify the device they're targeting, and receive optimized code for quick deployment. This approach encourages rapid innovation and accessibility in using AI across various devices, from smartphones to IoT applications.

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