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Yi Liu
Researcher at Beihang University
Publications - 58
Citations - 549
Yi Liu is an academic researcher from Beihang University. The author has contributed to research in topics: Computer science & Scalability. The author has an hindex of 8, co-authored 50 publications receiving 267 citations.
Papers
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Journal ArticleDOI
HitAnomaly: Hierarchical Transformers for Anomaly Detection in System Log
TL;DR: This article proposes HitAnomaly, a log-based anomaly detection model utilizing a hierarchical transformer structure to model both log template sequences and parameter values and assess the robustness of the proposed model on unstable log data.
Journal ArticleDOI
The Deep Learning Compiler: A Comprehensive Survey
Mingzhen Li,Yi Liu,Xiaoyan Liu,Qingxiao Sun,Xin You,Hailong Yang,Zhongzhi Luan,Lin Gan,Guangwen Yang,Depei Qian +9 more
TL;DR: This article performs a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations.
Journal ArticleDOI
The Deep Learning Compiler: A Comprehensive Survey
Mingzhen Li,Yi Liu,Xiaoyan Liu,Qingxiao Sun,Xin You,Hailong Yang,Zhongzhi Luan,Lin Gan,Guangwen Yang,Depei Qian +9 more
TL;DR: A comprehensive survey of DL compilers can be found in this article, with an emphasis on the DL oriented multi-level IRs and frontend/backend optimizations, and several insights are highlighted as the potential research directions of DL compiler.
Proceedings ArticleDOI
Allocating Tasks in Multi-core Processor based Parallel System
TL;DR: Evaluation result shows that the algorithm can find near-optimal solutions in reasonable time, and behaves better than genetic algorithm when the number of threads increases, since it can find solutions in much less time than Genetic algorithm.
Proceedings ArticleDOI
A Heuristic Energy-aware Scheduling Algorithm for Heterogeneous Clusters
Yu Li,Yi Liu,Depei Qian +2 more
TL;DR: A novel energy-aware task scheduling algorithm (EAMM) for heterogeneous clusters is proposed, which is based on the general adaptive scheduling heuristics Min-Min algorithm, and can achieve a good time-energy trade-off and outperform the original Min- Min algorithm under various conditions.