MINERVA - Minimal Inference Engine for Robust, Verifiable, and Authenticated ML. Encrypted, integrity-verified neural network inference for MCUs down to ATmega328P.
How fast kavishka-dot/libminerva is gaining stars, and where it lands on Trendshift and GitHub Trending
Activity and trending
Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending
Oct 30 – Nov 6, 2026
Stars +0in 8 days
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Forks +0in 8 days
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Merged PRs 0in 8 days
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Trendshift Trendingwhere the repository ranked each day it made the top 25
Not in the Trendshift top 25 in this period
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The left and right arrow keys step from day to day; Home and End go to the first and the last
Stars, forks, merged pull requests and Trendshift Trending rank, by day
Day
Stars gained
Forks gained
Merged PRs
Fri, Oct 30, 2026
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Sat, Oct 31, 2026
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Sun, Nov 1, 2026
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Thu, Nov 5, 2026
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Fri, Nov 6, 2026
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1#1 on the list
7Rank that day
Not trending
Social mentions
Recent discussions about this repository across the web
1 post
🤖 libminerva ⭐ 57 stars Run encrypted, verified neural networks on an 8-bit ATmega328P. Bring ironclad security to the most resource-constrained microcontrollers. 🔗 #AI #MachineLearning
Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).
Noodle provides primitive modular functions for convolution layer, dense layer, pooling and activations. It allows streaming the intermediate activations, weights, and biases from/to SD/FFat/SD_MMC filesystems to overcome RAM limitations.
