Changyao Lin

Orcid: 0000-0001-6805-2649

According to our database1, Changyao Lin authored at least 11 papers between 2021 and 2025.

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

Timeline

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Bibliography

2025
TOP: Task-Based Operator Parallelism for Asynchronous Deep Learning Inference on GPU.
IEEE Trans. Parallel Distributed Syst., February, 2025

2024
DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference.
IEEE Trans. Mob. Comput., October, 2024

A QoS-Aware Training Framework for ViT Compression, Partition, and Distillation.
Proceedings of the 32nd IEEE/ACM International Symposium on Quality of Service, 2024

COS: Cross-Processor Operator Scheduling for Multi-Tenant Deep Learning Inference.
Proceedings of the 32nd IEEE/ACM International Symposium on Quality of Service, 2024

Poster Abstract: Xpi: Real-Time Progressive Inference Serving with Explainable AI in Edge-Cloud Systems.
Proceedings of the 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, 2024

StressViT: Splitting and Compressing Vision Transformer Through Edge-Cloud Collaboration.
Proceedings of the Pattern Recognition - 27th International Conference, 2024

2023
BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms.
CoRR, 2023

POS: An Operator Scheduling Framework for Multi-model Inference on Edge Intelligent Computing.
Proceedings of the 22nd International Conference on Information Processing in Sensor Networks, 2023

2021
Using Householder's method to improve the accuracy of the closed-form formulas for implied volatility.
Math. Methods Oper. Res., 2021

ECSRL: A Learning-Based Scheduling Framework for AI Workloads in Heterogeneous Edge-Cloud Systems.
Proceedings of the SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems, Coimbra, Portugal, November 15, 2021

Choosing Appropriate AI-enabled Edge Devices, Not the Costly Ones.
Proceedings of the 27th IEEE International Conference on Parallel and Distributed Systems, 2021


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