All Outputs (4)

A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment (2023)
Presentation / Conference Contribution
Liu, X., Ma, L., Chen, Z., Zheng, C., Chen, R., Liao, Y., & Yang, S. (2023, December). A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment. Presented at 43rd SGAI International Conference on Artificial Intellige

Sparse-reward reinforcement learning environments pose a particular challenge because the agent receives infrequent rewards, making it difficult to learn an optimal policy. In this paper, we propose NSSE, a novel approach that combines that stratifie... Read More about A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment.

Building a Reusable and Extensible Automatic Compiler Infrastructure for reconfigurable devices (2023)
Presentation / Conference Contribution
Zang, Z., Dolinsky, U., Ghiglio, P., Cherubin, S., Goli, M., & Yang, S. (2023, September). Building a Reusable and Extensible Automatic Compiler Infrastructure for reconfigurable devices. Presented at FPL 2023: 33rd International Conference on Field-Progr

Multi-Level Intermediate Representation (MLIR) is gaining increasing attention in reconfigurable hardware communities due to its capability to represent various abstract levels for software compilers. This project aims to be the first to provide an e... Read More about Building a Reusable and Extensible Automatic Compiler Infrastructure for reconfigurable devices.

FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms (2023)
Presentation / Conference Contribution
Zhao, Y., Xia, Y., Loureiro, R., Zhao, H., Dolinsky, U., & Yang, S. (2023, September). FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms. Poster presented at FPL 2023: 33rd International Conference on Field-Programmable Logic

This demonstration showcases a novel FPGA development pipeline for developing a low-power and real-time motion artefact detection module for a wearable functional near-Infrared spectroscopy (fNIRS) processing system. We provide a brief overview of th... Read More about FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms.

Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset (2023)
Presentation / Conference Contribution
Zhao, Y., Xia, Y., Loureiro, R., Zhao, H., Dolinsky, U., & Yang, S. (2023). Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset. In IWOCL '23: Proceedings of the 2023 International Workshop on OpenCL. https://doi.org/1

Machine Learning has potential applications across a wide spectrum of devices. However, current approaches for domain-specific accelerators have encountered difficulties in satisfying the most recent computational demands for machine learning applica... Read More about Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset.