Nvidia patents method to shrink neural networks using codebooks
Nvidia describes a technique for reducing the size of neural networks by finding the best way to round their internal numbers. The system tests different scaling settings to match data values to a fixed list of reference points, minimizing the error introduced by this simplification.
In the filing’s own words
Apparatuses, systems, and techniques for calculating data compression parameters using codebook entry values. In at least one embodiment, one or more circuits is to calculate one or more data compression parameters based, at least in part, on at least on one or more values of the data to be compressed in relation to at least two codebook entry values.
What exactly they claim (claim 1)
1. One or more processors, comprising circuitry to: calculate a plurality of sets of candidate quantization scale parameters and one or more assignment parameters indicating an assignment of one or more data values to one or more codebook entry values based, at least in part, on minimization of one or more errors of quantizing the one or more data values to the one or more codebook entry values; select, from the plurality of sets of candidate quantization scale parameters, a set of quantization scale parameters that minimizes the one or more errors of quantizing the one or more data values; and generate a quantized version of a neural network using the selected set of quantization scale parameters.
Facts
- Company
- Nvidia (NVIDIA Corporation)
- Official title
- Calculating data compression parameters
- Patent number
- US12762751B2
- Application number
- 16952893
- Filed
- Nov 19, 2020
- Granted
- Oct 13, 2026
- Inventors
- 5
- Claims
- 33
Classified under
G06T 9/002 Physics & computingG06T 1/20 Physics & computingMore from Nvidia in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.