Summary of Learning Using a Single Forward Pass, by Aditya Somasundaram et al.
Learning Using a Single Forward Pass
by Aditya Somasundaram, Pushkal Mishra, Ayon Borthakur
First submitted to arxiv on: 15 Feb 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 This paper presents the Solo Pass Embedded Learning Algorithm (SPELA), a novel approach to overcome the limitations of traditional backpropagation in resource-constrained environments. SPELA’s rapid learning capabilities and local loss functions enable it to update weights efficiently, reducing the need for gradient propagation and computational graph storage. As a result, SPELA can accurately match backpropagation with significantly less data, computing power, and storage requirements. Additionally, SPELA can effectively fine-tune pre-trained image recognition models for new tasks. The authors’ results suggest that SPELA is an ideal candidate for learning in resource-constrained edge AI applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to learn called Solo Pass Embedded Learning Algorithm (SPELA). SPELA helps computers learn faster and use less power, memory, and data. This is important because some devices, like smart home appliances or smartphones, don’t have a lot of resources. SPELA can work with these limited resources while still getting good results. It’s also good at fine-tuning pre-trained models to do new tasks. The authors tested SPELA and found that it could be very useful for learning in situations where resources are limited. |
Keywords
» Artificial intelligence » Backpropagation » Fine tuning
