https://github.com/zkp-gravity/0g

https://github.com/zkp-gravity/0g

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/zkp-gravity/0g
• Owner: zkp-gravity
• Created: 2023-03-31T20:09:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-04-25T11:09:02.000Z (over 1 year ago)
• Last Synced: 2024-08-03T01:13:44.380Z (3 months ago)
• Language: Python
• Size: 21.8 MB
• Stars: 39
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-zkml - zkp-gravity/0g - ZeroGravity - Zero Gravity is a system for proving an inference run (i.e. a classification) for a pre-trained, public Weightless NN and a private input. (2023) (Codebases / Articles and podcasts)
• awesome-aleo - Zero Gravity - Proving an inference pass for a public Weightless Neural Network run on a private input. (Applications / Machine Learning)
• awesome-aleo - Zero Gravity - Proving an inference pass for a public Weightless Neural Network run on a private input. (Applications / Machine Learning)

README

        
![](zero-gravity-banner.png)

Zero Gravity is a system for proving an inference pass (i.e. a classification) for a pre-trained, public Weightless Neural Network run on a private input. Zero Gravity builds upon the recent BTHOWeN model by [Susskind et al (2022)](https://arxiv.org/abs/2203.01479), in which the authors improve upon earlier WNN models in a number of interesting ways.  Most importantly for this hackathon project, they helpfully provide an [implementation](https://github.com/ZSusskind/BTHOWeN) complete with pre-trained models and reproducible benchmarks.

See our [blog post](https://hackmd.io/@77sjNbqjST6HRnGPQyY9Dw/BkNGwbUW3) for an extensive description!

Built as part of the ZKHack hackathon, Lisbon, 2023.

## Setup

Clone and install the [custom aleo compiler]([email protected]:zkp-gravity/aleo-setup.git) supporting lookup arguments

## Usage

Values in the example below were generated using our fork of [BTHOWeN](https://github.com/zkp-gravity/BTHOWeN).

```

python3 scripts/generate_aleo_code.py 56

../aleo-setup/aleo/target/debug/aleo run main "$(cat input_file.txt)" "$(cat hash_values.txt)" "$(cat bloom_filters.txt)" $(cat winning_discriminator_value.txt) "$(cat winning_discriminator_index.txt)"

```

## Implementation notes and limitations

- All inputs are private, except for which discriminator (category) succeeded, as well as the model itself. 

- Proving time of e.g. a full MNIST example is not practical yet. PRs and suggestions for improvement are welcome.

- ❌ Print statements during the build phase are expected - and will be removed in future iterations of Aleo.

- The number of lookup constraints are not included in the final print statement but will show up when compiling in debug mode.

- Note that even when changing certain inputs, the proof might succeed, as it essentially checks which discriminator won.