Presentation

DescriptionDeep learning techniques have been shown to achieve superior performance on several arbitration tasks in computer hardware. However, these techniques cannot be directly implemented in hardware because of the prohibitive area and latency overhead. In this work, we propose a novel methodology to automatically "distill" the arbitration logic from simulation traces. We leverage tree-based models as a bridge to convert deep learning models to logic, and present a case study on a network-on-chip port arbitration task. The generated arbitration logic achieves significant reduction in average packet latency compared with the baselines.