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Found 6 papers, 1 papers with code
sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks
Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient.
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices.
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption.
TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources
In this work, we propose TinyLight, the first DRL-based ATSC model that is designed for devices with extremely limited resources.
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras.
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons
This paper proposes an unsupervised address event representation (AER) object recognition approach.
