Summary of Exmy: a Data Type and Technique For Arbitrary Bit Precision Quantization, by Aditya Agrawal and Matthew Hedlund and Blake Hechtman
eXmY: A Data Type and Technique for Arbitrary Bit Precision Quantization
by Aditya Agrawal, Matthew Hedlund, Blake Hechtman
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Numerical Analysis (math.NA)
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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 novel data type called eXmY is introduced to enable quantization of machine learning (ML) models, offering both arbitrary bit widths and formats. This allows for efficient compression, byte addressability, and sharding capabilities. The eXmY libraries support emulation, encoding, and decoding tensors and checkpoints in popular frameworks like C++, TensorFlow, JAX, and PAX. To optimize performance, the codecs utilize SIMD instructions on CPUs and vector instructions on TPUs and GPUs. The technique exploits statistical distributions of exponents in tensors to reduce memory usage, network traffic, and storage needs. This has been successfully deployed in production for almost two years. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary eXmY is a new way to make machine learning models smaller and faster. It lets you pick the right size and type of data to use, which helps with storing and moving information around. The eXmY system has libraries that work with popular computer programs like C++, TensorFlow, JAX, and PAX. To make it go fast, the system uses special instructions on computers and other devices. It’s already been used in real-life situations for almost two years. |
Keywords
» Artificial intelligence » Machine learning » Quantization
