Yuhao Liu |
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Phone Fax Visitor's Address |
+49 (0)351 463-43731 +49 (0)351 463-39995 Helmholtzstrasse 18, BAR-III78 |
Yuhao Liu is a research assistant. He obtained his B.Sc. degree in Electronic and Information Engineering from Zhengzhou University in China and his M.Sc. degree in Embedded System Engineering from the University of Duisburg-Essen in Germany. His current research interest is the implementation of FPGA-based Neural Network Accelerators.
LinkedIn: http://www.linkedin.com/in/yuhao-liu-horizon776
Publications
2025
- 7. Yuhao Liu, Salim Ullah, Akash Kumar, "BiKA: Binarized KAN-inspired Neural Network for Efficient Hardware Accelerator Designs", In Proceeding: 2025 IEEE 33rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 276-276, 2025. [doi] [Bibtex & Downloads]
BiKA: Binarized KAN-inspired Neural Network for Efficient Hardware Accelerator Designs
×Reference
Yuhao Liu, Salim Ullah, Akash Kumar, "BiKA: Binarized KAN-inspired Neural Network for Efficient Hardware Accelerator Designs", In Proceeding: 2025 IEEE 33rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 276-276, 2025. [doi]
Bibtex
@INPROCEEDINGS{11008950,
author={Liu, Yuhao and Ullah, Salim and Kumar, Akash},
booktitle={2025 IEEE 33rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)},
title={BiKA: Binarized KAN-inspired Neural Network for Efficient Hardware Accelerator Designs},
year={2025},
volume={},
number={},
pages={276-276},
keywords={Quantization (signal);Neuromorphic engineering;Computational modeling;Approximate computing;Artificial neural networks;Transforms;Complexity theory;Hardware acceleration;Field programmable gate arrays;fpga;hardware accelerator;approximate computing;kolmogorov-arnold network},
doi={10.1109/FCCM62733.2025.00036}}Downloads
No Downloads available for this publication
Permalink
- 6. Yuhao Liu, Salim Ullah, Akash Kumar, "Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-Precision Quantized Multiplication on Hardware Accelerators", In Proceeding: 2025 26th International Symposium on Quality Electronic Design (ISQED), pp. 1-9, 2025. [doi] [Bibtex & Downloads]
Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-Precision Quantized Multiplication on Hardware Accelerators
×Reference
Yuhao Liu, Salim Ullah, Akash Kumar, "Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-Precision Quantized Multiplication on Hardware Accelerators", In Proceeding: 2025 26th International Symposium on Quality Electronic Design (ISQED), pp. 1-9, 2025. [doi]
Bibtex
@INPROCEEDINGS{11014376,
author={Liu, Yuhao and Ullah, Salim and Kumar, Akash},
booktitle={2025 26th International Symposium on Quality Electronic Design (ISQED)},
title={Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-Precision Quantized Multiplication on Hardware Accelerators},
year={2025},
volume={},
number={},
pages={1-9},
keywords={Runtime;Quantization (signal);Accuracy;Neural networks;Memory architecture;Systolic arrays;Delays;Racetrack memory;Object tracking;Clocks},
doi={10.1109/ISQED65160.2025.11014376}}Downloads
BitSys_ISQED [PDF]
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2024
- 5. Maryam Eslami, Yuhao Liu, Salim Ullah, Mostafa E. Salehi, Reshad Hosseini, Seyed Ahmad Mirsalari, Akash Kumar, "MONO: Enhancing Bit-Flip Resilience With Bit Homogeneity for Neural Networks", In IEEE Embedded Systems Letters, Institute of Electrical and Electronics Engineers (IEEE), vol. 16, no. 4, pp. 333–336, Dec 2024. [doi] [Bibtex & Downloads]
MONO: Enhancing Bit-Flip Resilience With Bit Homogeneity for Neural Networks
×Reference
Maryam Eslami, Yuhao Liu, Salim Ullah, Mostafa E. Salehi, Reshad Hosseini, Seyed Ahmad Mirsalari, Akash Kumar, "MONO: Enhancing Bit-Flip Resilience With Bit Homogeneity for Neural Networks", In IEEE Embedded Systems Letters, Institute of Electrical and Electronics Engineers (IEEE), vol. 16, no. 4, pp. 333–336, Dec 2024. [doi]
Bibtex
@article{Eslami_2024, title={MONO: Enhancing Bit-Flip Resilience With Bit Homogeneity for Neural Networks}, volume={16}, ISSN={1943-0671}, url={http://dx.doi.org/10.1109/LES.2024.3444921}, DOI={10.1109/les.2024.3444921}, number={4}, journal={IEEE Embedded Systems Letters}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Eslami, Maryam and Liu, Yuhao and Ullah, Salim and Salehi, Mostafa E. and Hosseini, Reshad and Ahmad Mirsalari, Seyed and Kumar, Akash}, year={2024}, month=dec, pages={333–336} }Downloads
No Downloads available for this publication
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- 4. Yuhao Liu, Salim Ullah, Akash Kumar, "BitSys: Bitwise Systolic Array Architecture for Multi-precision Quantized Hardware Accelerators", In Proceeding: 2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), IEEE, pp. 220–220, May 2024. [doi] [Bibtex & Downloads]
BitSys: Bitwise Systolic Array Architecture for Multi-precision Quantized Hardware Accelerators
×Reference
Yuhao Liu, Salim Ullah, Akash Kumar, "BitSys: Bitwise Systolic Array Architecture for Multi-precision Quantized Hardware Accelerators", In Proceeding: 2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), IEEE, pp. 220–220, May 2024. [doi]
Bibtex
@inproceedings{Liu_2024, title={BitSys: Bitwise Systolic Array Architecture for Multi-precision Quantized Hardware Accelerators}, url={http://dx.doi.org/10.1109/fccm60383.2024.00042}, DOI={10.1109/fccm60383.2024.00042}, booktitle={2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)}, publisher={IEEE}, author={Liu, Yuhao and Ullah, Salim and Kumar, Akash}, year={2024}, month=may, pages={220–220} }Downloads
FCCM_Poster_Final_3 [PDF]
Permalink
2023
- 3. Yuhao Liu, Shubham Rai, Salim Ullah, Akash Kumar, "High Flexibility Designs of Quantized Runtime Reconfigurable Multi-Precision Multipliers", In IEEE Embedded Systems Letters, pp. 1-1, 2023. [doi] [Bibtex & Downloads]
High Flexibility Designs of Quantized Runtime Reconfigurable Multi-Precision Multipliers
×Reference
Yuhao Liu, Shubham Rai, Salim Ullah, Akash Kumar, "High Flexibility Designs of Quantized Runtime Reconfigurable Multi-Precision Multipliers", In IEEE Embedded Systems Letters, pp. 1-1, 2023. [doi]
Bibtex
@ARTICLE{10261986,
author={Liu, Yuhao and Rai, Shubham and Ullah, Salim and Kumar, Akash},
journal={IEEE Embedded Systems Letters},
title={High Flexibility Designs of Quantized Runtime Reconfigurable Multi-Precision Multipliers},
year={2023},
volume={},
number={},
pages={1-1},
doi={10.1109/LES.2023.3298736}}Downloads
ESL_LB_CASES_2023 Camera ready [PDF]
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- 2. Yuhao Liu, Shubham Rai, Salim Ullah, Akash Kumar, "NetPU-M: a Generic Reconfigurable Neural Network Accelerator Architecture for MLPs", In Proceeding: 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 85-92, 2023. [doi] [Bibtex & Downloads]
NetPU-M: a Generic Reconfigurable Neural Network Accelerator Architecture for MLPs
×Reference
Yuhao Liu, Shubham Rai, Salim Ullah, Akash Kumar, "NetPU-M: a Generic Reconfigurable Neural Network Accelerator Architecture for MLPs", In Proceeding: 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 85-92, 2023. [doi]
Bibtex
@INPROCEEDINGS{10196610,
author={Liu, Yuhao and Rai, Shubham and Ullah, Salim and Kumar, Akash},
booktitle={2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
title={NetPU-M: a Generic Reconfigurable Neural Network Accelerator Architecture for MLPs},
year={2023},
volume={},
number={},
pages={85-92},
doi={10.1109/IPDPSW59300.2023.00026}}Downloads
RAW2023-NetPU-M [PDF]
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2022
- 1. Yuhao Liu, Shubham Rai, Salim Ullah, Akash Kumar, "NetPU: Prototyping a Generic Reconfigurable Neural Network Accelerator Architecture", In Proceeding: 2022 International Conference on Field-Programmable Technology (ICFPT), pp. 1-1, Dec 2022. [doi] [Bibtex & Downloads]
NetPU: Prototyping a Generic Reconfigurable Neural Network Accelerator Architecture
×Reference
Yuhao Liu, Shubham Rai, Salim Ullah, Akash Kumar, "NetPU: Prototyping a Generic Reconfigurable Neural Network Accelerator Architecture", In Proceeding: 2022 International Conference on Field-Programmable Technology (ICFPT), pp. 1-1, Dec 2022. [doi]
Bibtex
@INPROCEEDINGS{9974206,
author={Liu, Yuhao and Rai, Shubham and Ullah, Salim and Kumar, Akash},
booktitle={2022 International Conference on Field-Programmable Technology (ICFPT)},
title={NetPU: Prototyping a Generic Reconfigurable Neural Network Accelerator Architecture},
year={2022},
month = {dec},
pages={1-1},
doi={10.1109/ICFPT56656.2022.9974206}}Downloads
NetPU_Prototyping_a_Generic_Reconfigurable_Neural_Network_Accelerator_Architecture [PDF]
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