Summary of Zero-shot Temporal Resolution Domain Adaptation For Spiking Neural Networks, by Sanja Karilanova et al.
Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks
by Sanja Karilanova, Maxime Fabre, Emre Neftci, Ayça Özçelikkale
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 In this paper, researchers introduce three novel domain adaptation methods to address a significant challenge in Spiking Neural Networks (SNNs): adapting neuron parameters to account for changes in time resolution without re-training. This is particularly important when deploying SNN models on neuromorphic devices, where energy efficiency and latency are crucial. The proposed methods utilize State Space Models (SSMs) to map neuron dynamics in SNNs and are applicable to general neuron models. Evaluations on audio keyword spotting datasets SHD and MSWC, as well as the image classification NMINST dataset, demonstrate that these methods outperform existing reference methods and enable high accuracy on high temporal resolution data using time-efficient training on lower temporal resolution data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SNNs are special kinds of deep neural networks that can learn from how our brains work. They’re really good at understanding sounds and images when they’re used in special computers called neuromorphic devices. But there’s a problem: these computers need to understand sounds and images at different speeds, which makes it hard for the SNNs to work well. The researchers in this paper found three new ways to make the SNNs work better in these situations. They tested their ideas on some special datasets, like recognizing sounds and classifying images, and found that they worked really well. |
Keywords
» Artificial intelligence » Domain adaptation » Image classification
