Collaborative Research: SHF: Small: Towards Robust Deep Learning Computing on GPUs

Graphics processing units (GPU) have become one of the most promising computing engines in many application domains such as scientific simulations and deep learning. With the massive parallel processing power provided by GPUs, most of the state-of-the-art server and edge systems employ GPUs as the core computing engines for deep-learning model training and inference.

As the performance of deep learning models becomes one of the most important delimiters that determines market revenue of the model creators and the convenience of daily lives of model consumers, it is critical to enforce reliable and robust deep-learning computation. This project aims to explore the challenges and opportunities to address the reliability and privacy implications of GPU computing as a deep-learning accelerator and design lightweight protection schemes.

The technical aims of this project are divided into three thrusts. The first thrust explores and evaluates possible vulnerabilities and their impact on GPU-based deep-learning computing. The second thrust tackles the vulnerabilities at the compute-unit level by redesigning GPU building blocks, such as new scheduling algorithms and activation acceleration logic.

The third thrust explores selective integrity protection mechanisms in communication channels and memory subsystems to transfer data between the CPU and GPU without imposing significant performance overhead. The proposed solutions will mitigate architectural and system vulnerabilities in GPU-based deep learning computing, which will enable the deep learning algorithm developers to focus more on performance improvement and technological advancement, and the consumers to use deep learning-based cognitive products without privacy concerns.

The findings of this research will be integrated into undergraduate and graduate courses as well as various outreach activities on K-12 education, and publicly shared through open-source repositories.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.