Yang Yang
Affiliations:- University of Southern California, Department of Electrical and Computer Engineering, Los Angeles, CA, USA
- University of California, Riverside, CA, USA
According to our database1,
Yang Yang
authored at least 14 papers
between 2012 and 2024.
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Bibliography
2024
Bandwidth Efficient Homomorphic Encrypted Discrete Fourier Transform Acceleration on FPGA.
Proceedings of the 32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2024
A Framework for Generating Accelerators for Homomorphic Encryption Operations on FPGAs.
Proceedings of the 35th IEEE International Conference on Application-specific Systems, 2024
2023
Proceedings of the 33rd International Conference on Field-Programmable Logic and Applications, 2023
2022
Bandwidth Efficient Homomorphic Encrypted Matrix Vector Multiplication Accelerator on FPGA.
Proceedings of the International Conference on Field-Programmable Technology, 2022
FPGA Accelerator for Homomorphic Encrypted Sparse Convolutional Neural Network Inference.
Proceedings of the 30th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2022
Proceedings of the CF '22: 19th ACM International Conference on Computing Frontiers, Turin, Italy, May 17, 2022
2021
Proceedings of the High Performance Computing - 36th International Conference, 2021
2020
Proceedings of the High Performance Computing - 35th International Conference, 2020
Proceedings of the International Conference on Field-Programmable Technology, 2020
Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020
How to Efficiently Train Your AI Agent? Characterizing and Evaluating Deep Reinforcement Learning on Heterogeneous Platforms.
Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020
2019
Proceedings of the Parallel Computing: Technology Trends, 2019
2013
Proceedings of the 23rd International Conference on Field programmable Logic and Applications, 2013
2012
An efficient dynamic multiple-candidate motion vector approach for GPU-based hierarchical motion estimation.
Proceedings of the 31st IEEE International Performance Computing and Communications Conference, 2012