Distilling Transformers and Diffusion Models for Robust Edge Use Cases with Fatih Porikli - #738
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.
“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.”