The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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

16 snips
Mar 14, 2022
Murali Akula, Sr. Director of Software Engineering at Qualcomm, leads innovations in AI for Snapdragon chips. He discusses the full-stack approach to AI development, emphasizing collaboration between research and deployment teams. The conversation uncovers challenges of deploying machine learning on mobile devices, including optimizing for power and memory constraints. Murali also highlights advancements like the X-Distill algorithm for depth estimation and the shift to localized AI training, showcasing how these breakthroughs are revolutionizing AI applications.
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INSIGHT

Qualcomm's Full-Stack AI

  • Qualcomm's "full stack" AI approach considers hardware, software, and algorithms for on-device deployment.
  • This full-stack optimization is crucial for deploying AI innovations on resource-constrained mobile devices.
INSIGHT

Mobile Device Constraints

  • Mobile devices have unique constraints like power efficiency, real-time latency, and limited chip area.
  • Optimizing neural networks for these constraints involves trade-offs between performance and resources.
ANECDOTE

Monocular Depth Estimation Example

  • Qualcomm implemented monocular depth estimation, showcasing their full-stack approach.
  • The X-Distil method improved training accuracy without increasing inference complexity.
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