Tinn

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Tinn is a tiny neural network library written in C99, designed for lightweight AI applications. It is dependency-free and consists of approximately 200 lines of code, making it highly portable.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is tinn?

Tinn (Tiny Neural Network) is a minimalist, dependency-free neural network library implemented in C99. Comprising fewer than 200 lines of code, it offers a highly portable solution for integrating AI capabilities into various systems, including embedded devices. Tinn supports sigmoidal activation and a single hidden layer, making it suitable for tasks like hand-written digit recognition, where it can achieve over 99% accuracy. Developers can train models on powerful machines and deploy them to microcontrollers for real-time event prediction. The library emphasizes minimalism, providing core neural network functionality without extensive features found in larger libraries, and includes tips for optimizing training and usage.

Best used for

Ideal for students and developers who need to implement basic neural networks, deploy AI models on resource-constrained embedded systems, and learn the fundamentals of machine learning. Especially valuable for educational purposes and small-scale AI projects requiring high portability and minimal dependencies.

Common actions

implement neural networks

deploy AI models

learn machine learning

develop embedded AI

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Capabilities

Key features

Dependency-free C99 library
Sigmoidal activation
One hidden layer
Highly portable
Under 200 lines of code

Target Audience

studentteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of neural network architectures does Tinn support?

Tinn is designed for simplicity, supporting a basic feed-forward neural network architecture with sigmoidal activation and a single hidden layer. This minimalist approach makes it efficient for specific tasks and embedded applications, rather than complex deep learning models.

Can Tinn be used for real-time applications on microcontrollers?

Yes, Tinn is highly suitable for embedded systems and real-time applications. Its small footprint and C99 implementation allow developers to train models on powerful desktops and then load them onto microcontrollers to predict real-time events using analog-to-digital converters.

Does Tinn handle random number generation and data shuffling automatically?

No, Tinn does not seed the random number generator or shuffle input data automatically. Users are advised to seed the random number generator themselves and shuffle their input data after every training iteration to ensure effective and unbiased model training.

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