Summary of Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models, by Rishav Mukherji et al.
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models
by Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer, Christian Mayr, David Kappel, Anand Subramoney
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 investigates the effects of combining activity sparsity and weight pruning in spiking neural networks (SNNs) on complex sequence tasks like language modeling. The authors use a recently published SNN-like architecture that performs well on small-scale language modeling and study the trade-off between efficiency gains from the combination and task performance for large-scale language modeling datasets like Penn Treebank and WikiText-2. They demonstrate that sparse activity and connectivity complement each other without a proportional drop in task performance, making sparsely connected event-based neural networks promising candidates for efficient sequence modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make neural networks more efficient by using two methods: making some neurons only work sometimes (activity sparsity) and getting rid of some connections between them (weight pruning). The authors test this combination on language models, which are hard for some types of artificial intelligence to do well. They use a special kind of neural network that works better than usual ones for small tasks, but still want to know if it can handle bigger tasks. By comparing the results of normal and sparse networks, they show that making neurons only work sometimes and getting rid of connections helps without hurting how well the model does its job. |
Keywords
» Artificial intelligence » Neural network » Pruning
