Selected Publications
LSROM: Learning Self-Refined Organizing Map for Fast Imbalanced Streaming Data Clustering
IEEE Transactions on Neural Networks and Learning System (TNNLS), 2024, under review
We propose an efficient approach called Learning Self-Refined Organizing Map (LSROM) to handle the imbalanced streaming data clustering problem.
Accelerating Frequency-Domain Convolutional Neural Networks Inference Using FPGAs
IEEE International Symposium on Circuits and Systems (ISCAS), 2024
We present an FPGA-based 8-bit inference accelerator that packs frequency-domain calculations into digital signal processing (DSP) blocks to fully utilize DSPs for performance boost.
Bitwidth-aware Block Floating Point Quantization for Deep Neural Network Inference on Embedded Platforms
ACM International Conference on Multimedia (ACM MM), 2024, under review
We develop a BFP-based bitwidth-ware analytical modeling framework (called “BitQ”) for the best implementation of DNN inference on embedded platforms.
Accelerating event-based deep neural networks via flexible data encoding
IEICE Electronics Express (IEICE), 2023
We provide a dataflow that enables flexible DNN data encodings based on the event data characteristic for energy saving.
TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics
Journal of Molecular Biology (JMB), 2024
We propose Triple-Enhancement based Graph Neural Network (TENET), in which three progressive enhancement mechanisms build upon each other to create a cumulative effect.
Patents
FPGA-based mixed-precision data frequency domain convolution acceleration method and system
YGZS231122AF332
Frequency domain convolution operation acceleration system for 8-bit frequency domain convolutional neural network
YGZS2214229AF286
Awards
- Huawei Smart Base Scholarship Award (2023)
- Mingdong Scholarship Award (2023)
- Mingdong Scholarship Award (2022)