Yuchang Huang
Master's Student in Information Science and Engineering
Computing Architecture Laboratory
Nara Institute of Science and Technology, Japan
My research focuses on spiking neural networks, efficient Transformer architectures, neuromorphic computing, and surface electromyography-based human gesture recognition. I am particularly interested in developing energy-efficient artificial intelligence systems for biosignal understanding.
About Me
I am a master's student at the Nara Institute of Science and Technology (NAIST) and a member of the Computing Architecture Laboratory. My current research investigates efficient deep learning architectures for biosignal processing, with an emphasis on spiking neural networks and sEMG-based gesture recognition.
Before joining NAIST, I received my bachelor's degree from Yunnan University. My broader research goal is to bridge high-performance representation learning and energy-efficient neuromorphic computation.
Research Interests
Spiking Neural Networks
Event-driven neural computation, spike encoding, firing-rate control, and efficient temporal modeling.
Efficient Transformers
Lightweight attention mechanisms and efficient architectures for edge and neuromorphic systems.
Biosignal Understanding
Surface electromyography, human gesture recognition, and physiological time-series analysis.
Neuromorphic Computing
Low-power artificial intelligence and hardware-aware neural network design.
Publications
STGesture: A Topology-Aware Spiking Transformer for Sparse sEMG-Based Gesture Recognition
Manuscript in preparation.
More publications will be added here.
This section will be updated as new research results become available.
Research Projects
STGesture
A topology-aware Spiking Transformer framework for efficient sparse sEMG gesture recognition.
Spiking Transformer sEMG Gesture RecognitionTopology-Aware Spike Encoding
Spike encoding methods designed to preserve local electrode relationships and stabilize neural activity.
Spike Encoding Electrode TopologyLocal-Global Spike Attention
Lightweight local and global spike interaction mechanisms for reducing the computational cost of attention.
Attention Energy EfficiencyNeuromorphic Biosignal Processing
Energy-efficient neural architectures for processing temporal physiological signals on edge devices.
Neuromorphic AI Edge ComputingEducation
Nara Institute of Science and Technology
Master's Student, Information Science and Engineering
Computing Architecture Laboratory, Nara, Japan
Yunnan University
Bachelor's Degree
Kunming, China
Contact
Email: yuchang.huang.yf6@naist.ac.jp
GitHub: github.com/StubbornDuck
Affiliation: Computing Architecture Laboratory, Nara Institute of Science and Technology
Location: Nara, Japan