Garrison: A High-Performance GPU-Accelerated Inference System for Adversarial Ensemble Defense
Yan Wang, Xingbin Wang, Zechao Lin, Yulan Su, Sisi Zhang, Rui Hou, Dan Meng · 2024
In the face of huge threats from adversarial attacks, developing an efficient defense mechanism is crucial for deep learning systems. Adversarial ensemble defense method is one of the most effective techniques for defending against adversarial attacks, which constructs ensembles of multiple DNNs to improve model's robustness. However, deploying ensemble defense methods on existing DNN inference systems is inefficient and impractical due to their dynamics and randomness. To this end, we propose an inference system for adversarial ensemble defense called Garrison, which can deliver robust and low-latency predictions using Multi-Instance GPUs. Garrison employs a multi-granularity GPU partitioning strategy, optimizing hardware utilization by capitalizing on the intrinsic heterogeneity of GPUs. It also integrates a reinforcement learning-based scheduling mechanism, enabling random ensemble of diverse defense models to enhance robustness while maintaining bounded latency. Our evaluations show that Garrison can improve adversarial robustness by up to 24.5%, while accelerating ensemble inference by up to 6.6X compared to the state-of-the-art inference framework.