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Found 5 papers, 2 papers with code
SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer
As the third-generation neural network, the Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency, making it suitable for implementation on edge devices.
Enabling energy-Efficient object detection with surrogate gradient descent in spiking neural networks
In this study, we introduce the Current Mean Decoding (CMD) method, which solves the regression problem to facilitate the training of deep SNNs for object detection tasks.
SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs
Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performance of SNNs is still behind Artificial Neural Networks (ANNs).
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks
This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness.
High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning
Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding.
