Summary of A Framework to Enable Algorithmic Design Choice Exploration in Dnns, by Timothy L. Cronin Iv et al.
A Framework to Enable Algorithmic Design Choice Exploration in DNNs
by Timothy L. Cronin IV, Sanmukh Kuppannagari
First submitted to arxiv on: 10 Oct 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 Deep learning technologies have seen significant success across various domains, driven by advancements in algorithms behind deep neural networks (DNNs). These enhanced algorithms hold the potential to greatly increase DNN performance. However, discovering the best-performing algorithm for a DNN and adapting it to use that algorithm is a challenging and time-consuming task. To address this, we introduce an open-source framework providing fine-grained algorithmic control for DNNs, enabling algorithm exploration and selection. The framework includes built-in high-performance implementations of common deep learning operations and allows users to implement and select their own algorithms. This framework’s accelerated implementations yield outputs equivalent to those in PyTorch, a popular DNN framework, with no additional performance overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning is super cool because it lets computers learn from big amounts of data! But sometimes finding the right way for these computers to learn is really hard and time-consuming. To help solve this problem, scientists created an open-source tool that makes it easy to try out different ways for deep learning computers to work. This tool also has some pre-made solutions that are just as good as what other popular tools can do. The best part? It doesn’t slow down the computer at all! |
Keywords
* Artificial intelligence * Deep learning
