Summary of Nachos: Neural Architecture Search For Hardware Constrained Early Exit Neural Networks, by Matteo Gambella et al.
NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks
by Matteo Gambella, Jary Pomponi, Simone Scardapane, Manuel Roveri
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 A new type of neural network, Early Exit Neural Networks (EENNs), combines a standard Deep Neural Network (DNN) with Early Exit Classifiers (EECs) to provide predictions at intermediate points. This approach offers numerous benefits in terms of effectiveness and efficiency. However, designing EENNs manually is a complex task that requires expertise and can be time-consuming. To address this challenge, researchers are exploring the application of Neural Architecture Search (NAS) to automate the design process. Unfortunately, few comprehensive NAS solutions for EENNs have been proposed, leaving room for innovation. This work presents NACHOS, the first NAS framework specifically designed for the joint optimization of backbone and EECs in EENNs, while satisfying constraints on accuracy and MAC operations. The results demonstrate that the models generated by NACHOS are competitive with state-of-the-art EENNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Early Exit Neural Networks (EENNs) are a new kind of neural network that helps computers make predictions faster and more accurately. They work by giving an early answer when they’re sure about what something is, instead of waiting until the very end. This can be really helpful for things like image recognition or natural language processing. The problem is that designing these networks manually is hard and takes a lot of time. That’s why scientists are trying to use computers to help design them automatically. This new method, called NACHOS, is the first to do this in a special way that makes sure the network works well and uses the right amount of computer power. The results show that these automatic designs work just as well as the ones designed by experts. |
Keywords
* Artificial intelligence * Natural language processing * Neural network * Optimization
