Fanrong Li

Orcid: 0000-0002-4626-5026

According to our database1, Fanrong Li authored at least 14 papers between 2018 and 2024.

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

Timeline

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Links

On csauthors.net:

Bibliography

2024
MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization.
Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2024

2023
Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification.
IEEE Trans. Neural Networks Learn. Syst., August, 2023

Improving Extreme Low-Bit Quantization With Soft Threshold.
IEEE Trans. Circuits Syst. Video Technol., April, 2023

A<sup>2</sup>Q: Aggregation-Aware Quantization for Graph Neural Networks.
CoRR, 2023

$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Block Convolution: Toward Memory-Efficient Inference of Large-Scale CNNs on FPGA.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022

GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA.
CoRR, 2021

Dynamic Dual Gating Neural Networks.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

EBERT: Efficient BERT Inference with Dynamic Structured Pruning.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020
FSA: A Fine-Grained Systolic Accelerator for Sparse CNNs.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020

Grasp State Assessment of Deformable Objects Using Visual-Tactile Fusion Perception.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2019
A System-Level Solution for Low-Power Object Detection.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

2018
Block convolution: Towards memory-efficient inference of large-scale CNNs on FPGA.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018


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