glouw/tinn

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2133 stars · 189 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Tinn is a tiny, dependency-free neural network library written in under 200 lines of C99, implementing a feedforward network with one hidden layer and sigmoidal activation trained via backpropagation. It is designed for portability and embedded systems, where a model can be trained on a desktop and loaded onto a microcontroller for inference.

Use cases

• train a tiny neural network in C for handwritten digit recognition
• run neural network inference on a microcontroller or embedded device
• learn how backpropagation works by reading a minimal implementation
• embed a small feedforward network in a C or C++ project without dependencies
• predict real-time sensor events from an ADC using a pretrained model
• use a neural network where only the C standard library is available

When to choose

• you need an extremely small, dependency-free neural network for embedded or constrained environments
• you want readable, minimal source code to study or modify neural network fundamentals
• your model fits a simple feedforward architecture with one hidden layer

When to avoid

• you need deep architectures, convolutions, GPUs, or modern training features
• you want a fully featured neural network library like Kann, Genann, PyTorch, or TensorFlow
• you need built-in multithreading, random seeding, or data shuffling utilities

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning embedded-systems developer-tools cross-platform embedded c neural-network feedforward backpropagation minimalist single-hidden-layer c99 dependency-free microcontroller

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