mohammad-ghaderi/cat-dog-asm-cnn

A Convolutional Neural Network implemented entirely from scratch in x86-64 assembly using AVX-512, performing cat vs dog image classification without any ML frameworks or libraries.

45
/ 100
Emerging

This project helps deeply understand how Convolutional Neural Networks (CNNs) classify images like distinguishing between cats and dogs, by showing the fundamental, low-level computations involved. It takes raw image data as input and produces a classification (cat or dog) as output, offering insights into explicit parallel computation and memory management. This would be used by deep learning researchers or hardware architects who need to optimize machine learning models at the CPU instruction level.

168 stars.

Use this if you are a deep learning researcher or hardware engineer looking to understand and optimize CNN performance at the lowest possible level, including explicit SIMD instructions.

Not ideal if you are an application developer or data scientist looking for a high-level, easy-to-use framework for image classification.

deep-learning-optimization computer-vision-hardware assembly-programming neural-network-architecture performance-engineering
No Package No Dependents
Maintenance 10 / 25
Adoption 10 / 25
Maturity 13 / 25
Community 12 / 25

How are scores calculated?

Stars

168

Forks

14

Language

Assembly

License

MIT

Last pushed

Feb 08, 2026

Commits (30d)

0

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