Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning

Abstract

Dynamic resources management in reconfigurable processors often manifests as a hard online decision-making task, which should yield premier solutions that must meet Quality-of-Service (QoS) requirements while maximizing the system’s efficiency. Most prior works rely on a hard-to-train predictor to model the complicated relationships between processor configurations and performance. To decide the proper resource allocation, the predictor needs to tentatively evaluate a group of possible configurations, and then decide the best configuration for the workload. This tedious process has an expensive runtime overhead for resource configuration in processors. Besides, prior works focus on improving the prediction accuracy, however, higher performance prediction cannot guarantee a good system outcome. Inspired by recent advances in adversarial learning, we present a generative adversarial network (GAN)-based framework, Amphis, which can directly generate the on-demand processor configuration for any scheduled-in application. By evaluating Amphis on a reconfigurable processor with 18 different workloads, our results demonstrate that the GAN-based method provides tremendous overhead reduction (up to 90%) compared to the SOTA prediction-based method WNNM while providing higher resource utilization.