Summary of Towards a Tailored Mixed-precision Sub-8-bit Quantization Scheme For Gated Recurrent Units Using Genetic Algorithms, by Riccardo Miccini et al.
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms
by Riccardo Miccini, Alessandro Cerioli, Clément Laroche, Tobias Piechowiak, Jens Sparsø, Luca Pezzarossa
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Signal Processing (eess.SP)
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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 proposed modular integer quantization scheme for Gated Recurrent Units (GRUs) enables independent selection of bit widths for each operator. This allows for mixed-precision solutions that can outperform homogeneous-precision models in terms of Pareto efficiency. By employing Genetic Algorithms to explore the vast search space, researchers optimize model size and accuracy simultaneously. The scheme is evaluated on four sequential tasks, achieving a model size reduction of 25% to 55% while maintaining comparable accuracy to an 8-bit homogeneous equivalent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers found a way to make deep learning models work better on small devices that use very little power. They made a new way to shrink these models without losing their ability to do tasks correctly. This works by letting each part of the model have its own number of “bits” (like digital phone keys), and then using a special search tool to find the best combination of bits for each part. The team tested this method on four different tasks and found that it can make models 25% to 55% smaller while keeping them just as good at doing their jobs. |
Keywords
* Artificial intelligence * Deep learning * Precision * Quantization
