Bo Liu

Orcid: 0000-0002-8908-0777

Affiliations:
  • Huazhong University of Science and Technology, School of Computer Science and Technology, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, National Engineering Research Center for Big Data Technology and System, Wuhan, China


According to our database1, Bo Liu authored at least 12 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
LayCO: Achieving Least Lossy Accuracy for Most Efficient RRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization.
J. Comput. Sci. Technol., April, 2023

TOSA: Tolerating Stuck-At-Faults in Edge-based RRAM Inference Accelerators.
Proceedings of the 29th IEEE International Conference on Parallel and Distributed Systems, 2023

2021
Bridging the Gap Between Memory and Communication Efficiency on Distributed Deep Learning Systems.
IEEE Access, 2021

Improving the energy efficiency of STT-MRAM based approximate cache.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

2020
Layup: Layer-adaptive and Multi-type Intermediate-oriented Memory Optimization for GPU-based CNNs.
ACM Trans. Archit. Code Optim., 2020

GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning.
Proceedings of the Green, Pervasive, and Cloud Computing - 15th International Conference, 2020

2019
Per-File Secure Deletion for Flash-Based Solid State Drives.
Proceedings of the 2019 IEEE International Conference on Networking, 2019

An Efficient Design and Implementation of Deduplication on Open-Channel SSDs.
Proceedings of the 21st IEEE International Conference on High Performance Computing and Communications; 17th IEEE International Conference on Smart City; 5th IEEE International Conference on Data Science and Systems, 2019

2018
Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures.
ACM Trans. Archit. Code Optim., 2018

Graph Processing on GPUs: A Survey.
ACM Comput. Surv., 2018

Layrub: layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems.
Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018

2016
Measuring Directional Semantic Similarity with Multi-features.
Proceedings of the Web Technologies and Applications - 18th Asia-Pacific Web Conference, 2016


  Loading...