RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models
This article introduces the enhanced Runtime Analyzing and Visualization Simulator (RAVSim) v2.0, a graphical tool that not only supports SNN design and analysis but also facilitates a comprehensive comparative analysis of various SNN models. The new version of RAVSim introduces a groundbreaking fea...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711024003765 |
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| Summary: | This article introduces the enhanced Runtime Analyzing and Visualization Simulator (RAVSim) v2.0, a graphical tool that not only supports SNN design and analysis but also facilitates a comprehensive comparative analysis of various SNN models. The new version of RAVSim introduces a groundbreaking feature enabling users to conduct in-depth comparisons of SNN models, enhancing understanding and aiding in model selection for specific applications. Furthermore, with the updated version of RAVSim, researchers, and developers can effortlessly generate trained model weights using a custom dataset, eliminating the need to investigate or write complicated backend code. This new feature facilitates the seamless integration of diverse datasets, streamlining the process for further analysis and exploration. Therefore, the developers can now focus on high-level tasks and gain a clear understanding of SNN without worrying about the technical complexities of weight generation. This advancement represents a significant step towards making SNNs more accessible and user-friendly, unlocking their full potential in artificial intelligence and computational neuroscience applications. Furthermore, RAVSim’s code has undergone extensive optimization and debugging, leading to a substantial ∼65% reduction in image classification simulation time compared to the previous RAVSim version. This improvement makes it easier and quicker to train models and generate weights. |
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| ISSN: | 2352-7110 |