20 citations · 39 across the 8 of their papers we have counts for
8 papers
ED-sKWS: Early-Decision Spiking Neural Networks for Rapid,and Energy-Efficient Keyword Spotting
Zeyang Song, Qianhui Liu, Qu Yang +2
Keyword Spotting (KWS) is essential in edge computing requiring rapid and energy-efficient responses. Spiking Neural Networks (SNNs) are well-suited for KWS for their efficiency an…
sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks
Qu Yang, Qianhui Liu, Nan Li +3
Speech applications are expected to be low-power and robust under noisy conditions. An effective Voice Activity Detection (VAD) front-end lowers the computational need. Spiking Neu…
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
Qu Yang, Malu Zhang, Jibin Wu +2
The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biol…
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
Shimin Zhang, Qu Yang, Chenxiang Ma +3
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a…
Long Short-term Memory with Two-Compartment Spiking Neuron
Shimin Zhang, Qu Yang, Chenxiang Ma +3
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a…
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
Xinyi Chen, Qu Yang, Jibin Wu +2
Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneo…
Training Spiking Neural Networks with Local Tandem Learning
Qu Yang, Jibin Wu, Malu Zhang +3
Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient over their predecessors. However, there is a lack of an efficient and generalized tr…
Deep Spiking Neural Network with Spike Count based Learning Rule
Jibin Wu, Yansong Chua, Malu Zhang +3
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synch…