Summary of From a Lossless (~1.5:1) Compression Algorithm For Llama2 7b Weights to Variable Precision, Variable Range, Compressed Numeric Data Types For Cnns and Llms, by Vincenzo Liguori
From a Lossless (~1.5:1) Compression Algorithm for Llama2 7B Weights to Variable Precision, Variable Range, Compressed Numeric Data Types for CNNs and LLMs
by Vincenzo Liguori
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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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 proposes a novel lossless compression algorithm for the weights of Large Language Model (LLM) Llama2 7B, which can be implemented in approximately 200 look-up tables (LUTs) on AMD FPGAs. This framework is then extended to support variable precision, variable range, and compressed numerical data types, building upon both floats and posits. The authors also discuss a simple hardware implementation of this format based on Asymmetrical Numeral Systems (ANS), which enables bandwidth reduction while bridging the gap between the flexible data format and a computational engine. Additionally, an example of a token factory using weight compression and sharing is provided. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to shrink the size of Large Language Model weights without losing any information. This helps computers process language models faster and more efficiently. The method works by compressing the numbers that make up the model’s weights, which are used for tasks like text understanding. The authors also show how this compression can be done in hardware using a special kind of number system called Asymmetrical Numeral Systems. This could help speed up computer processing for language-based tasks. |
Keywords
» Artificial intelligence » Large language model » Precision » Token
