Symbolic Distillation for Learned TCP Congestion Control

1 code implementation • 24 Oct 2022 • S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang

At the core of our proposal is a novel symbolic branching algorithm that enables the rule to be aware of the context in terms of various network conditions, eventually converting the NN policy into a symbolic tree.

Reinforcement Learning (RL)

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

no code implementations • 25 Oct 2021 • Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu

Today's auto-tuners (e. g., AutoTVM, Ansor) generate efficient tensor programs by navigating a large search space to identify effective implementations, but they do so with opaque hardware details.