Summary of Integer-valued Training and Spike-driven Inference Spiking Neural Network For High-performance and Energy-efficient Object Detection, by Xinhao Luo et al.
Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection
by Xinhao Luo, Man Yao, Yuhong Chou, Bo Xu, Guoqi Li
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper bridges the performance gap between Artificial Neural Networks (ANNs) and Brain-inspired Spiking Neural Networks (SNNs) on object detection tasks. It proposes a SpikeYOLO architecture, which simplifies the vanilla YOLO and incorporates meta SNN blocks to address spike degradation issues. Additionally, it designs a new spiking neuron that activates Integer values during training while maintaining spike-driven behavior during inference, mitigating quantization errors. The method is validated on static (COCO) and neuromorphic (Gen1) object detection datasets, achieving state-of-the-art performance with improved energy efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make Brain-inspired Spiking Neural Networks work better for tasks like recognizing objects in pictures. They created a new way to build these networks that fixes some problems with how they process information. This makes their networks perform better and use less energy. The results show that this new approach is really good at detecting objects, even compared to more traditional Artificial Neural Networks. |
Keywords
» Artificial intelligence » Inference » Object detection » Quantization » Yolo
