Tengxiao Wang
Orcid: 0009-0005-8335-0712
According to our database1,
Tengxiao Wang
authored at least 16 papers
between 2020 and 2025.
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Collaborative distances:
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Bibliography
2025
MorphBungee-Lite: An Edge Neuromorphic Architecture With Balanced Cross-Core Workloads Based on Layer-Wise Event-Batch Learning/Inference.
IEEE Trans. Circuits Syst. II Express Briefs, January, 2025
2024
A Novel Multiple-Input Multiple-Output OCDM Transmission System Based on Index Modulation.
Proceedings of the 16th International Conference on Wireless Communications and Signal Processing, 2024
2023
Modeling the Global Relationship via the Point Cloud Transformer for the Terrain Filtering of Airborne LiDAR Data.
Remote. Sens., December, 2023
An Edge Neuromorphic Hardware With Fast On-Chip Error-Triggered Learning on Compressive Sensed Spikes.
IEEE Trans. Circuits Syst. II Express Briefs, July, 2023
MorphBungee: A 65nm 7.2mm<sup>2</sup> 27μJ/image Digital Edge Neuromorphic Chip with On-Chip 802 Frame/s Multi-Layer Spiking Neural Network Learning.
Proceedings of the IEEE Asian Solid-State Circuits Conference, 2023
Live Demonstration: Face Recognition at The Edge Using Fast On-Chip Deep Learning Neuromorphic Chip.
Proceedings of the 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2023
2022
A Low-Cost FPGA Implementation of Spiking Extreme Learning Machine With On-Chip Reward-Modulated STDP Learning.
IEEE Trans. Circuits Syst. II Express Briefs, 2022
TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity.
IEEE Trans. Biomed. Circuits Syst., 2022
TEDOP: A Tiny Event-Driven Neural Network Hardware Core Enabling On-Chip Spike-Driven Synaptic Plasticity.
Proceedings of the 2022 IEEE International Conference on Integrated Circuits, 2022
MorphBungee: An Edge Neuromorphic Chip for High-Accuracy On-Chip Learning of Multiple-Layer Spiking Neural Networks.
Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2022
2021
DeepTempo: A Hardware-Friendly Direct Feedback Alignment Multi-Layer Tempotron Learning Rule for Deep Spiking Neural Networks.
IEEE Trans. Circuits Syst. II Express Briefs, 2021
CompSNN: A lightweight spiking neural network based on spatiotemporally compressive spike features.
Neurocomputing, 2021
A Heterogeneous Spiking Neural Network for Computationally Efficient Face Recognition.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2021
TripleBrain: An Edge Neuromorphic Architecture for High-accuracy Single-layer Spiking Neural Network with On-chip Self-organizing and Reinforcement Learning.
Proceedings of the 2021 IEEE International Conference on Integrated Circuits, 2021
Proceedings of the Cyber Security Intelligence and Analytics, 2021
2020
A High-Speed Low-Cost VLSI System Capable of On-Chip Online Learning for Dynamic Vision Sensor Data Classification.
Sensors, 2020