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Found 10 papers, 9 papers with code
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
To address this issue, we first introduce the Spike Voxel Coding (SVC) scheme, which encodes the 3D point clouds into a sparse spike train space, reducing the storage requirements and saving time on point cloud preprocessing.
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
This work enables SNNs to match ANN performance while maintaining the low-power advantage, marking a significant step towards SNNs as a general visual backbone.
MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map
Various linear complexity models, such as Linear Transformer (LinFormer), State Space Model (SSM), and Linear RNN (LinRNN), have been proposed to replace the conventional softmax attention in Transformer structures.
Scalable Autoregressive Image Generation with Mamba
On the ImageNet1K 256*256 benchmark, our best AiM model achieves a FID of 2. 21, surpassing all existing AR models of comparable parameter counts and demonstrating significant competitiveness against diffusion models, with 2 to 10 times faster inference speed.
Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection
The proposed method is validated on both static and neuromorphic object detection datasets.
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding
Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature.
High-Performance Temporal Reversible Spiking Neural Networks with $O(L)$ Training Memory and $O(1)$ Inference Cost
This work is expected to break the technical bottleneck of significantly increasing memory cost and training time for large-scale SNNs while maintaining high performance and low inference energy cost.
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency.
Ranked #112 on
Image Classification
on CIFAR-10
Deep Directly-Trained Spiking Neural Networks for Object Detection
Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics.
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network
LTH opens up a new path for network pruning.
