Distilling Transformers and Diffusion Models for Robust Edge Use Cases with Fatih Porikli - #738
Published:Jul 9, 2025 15:53
•1 min read
•Practical AI
Analysis
This article from Practical AI discusses Qualcomm's research presented at the CVPR conference, focusing on the application of AI models for edge computing. It highlights two key projects: "DiMA," an autonomous driving system that utilizes distilled large language models to improve scene understanding and safety, and "SharpDepth," a diffusion-distilled approach for generating accurate depth maps. The article also mentions Qualcomm's on-device demos, showcasing text-to-3D mesh generation and video generation capabilities. The focus is on efficient and robust AI solutions for real-world applications, particularly in autonomous driving and visual understanding, demonstrating a trend towards deploying complex models on edge devices.
Key Takeaways
- •Qualcomm is actively researching and developing AI solutions for edge computing.
- •The research focuses on distilling complex models like LLMs and diffusion models for efficiency and robustness.
- •Applications include autonomous driving, depth estimation, and on-device generative AI.
Reference
“We start with “DiMA: Distilling Multi-modal Large Language Models for Autonomous Driving,” an end-to-end autonomous driving system that incorporates distilling large language models for structured scene understanding and safe planning motion in critical "long-tail" scenarios.”