Old-school C neural nets for when you don't need a GPU

What it does This is a no-frills C library for building, training, and running multilayer artificial neural networks. It supports both fully connected and sparsely connected topologies, manages training data through a dedicated framework, and can execute in fixed-point (int) or floating-point (float/double) arithmetic. The entire workflow—create, train, run—is exposed through a minimal API, and complete networks can be saved and loaded from disk.

The interesting bit FANN targets constraints that modern frameworks usually ignore: cache-optimized routines, stepwise linear activations for speed, and true fixed-point support for platforms without an FPU. That makes it genuinely embeddable in a way that a modern Python-based framework rarely is. The Cascade2 algorithm, which dynamically grows network topology during training, is also a throwback to when architecture search was part of the training process itself.

Key highlights

• Backpropagation variants including RPROP, Quickprop, batch, and incremental training
• Bindings to more than 15 languages, so you are not trapped in C
• LGPL license, meaning commercial use is explicitly allowed
• Cross-platform support spanning Linux, Unix, and Windows, with project files for MSVC++ and Borland compilers reported to work
• Graphical interfaces and a 50-page university report documenting implementation trade-offs

Caveats

• The README claims execution is “up to 150 times faster than other libraries” without naming competitors, benchmarks, or hardware, so the basis for that figure is unclear.
• It lists Borland compiler support and cites “approximately 100 downloads a day,” both of which suggest the project’s public-facing metrics and platform targets may be dated.
• Sparse connectivity and fixed-point execution are advertised, but the README does not explain how to configure either.

Verdict Worth a look if you need a small, dependency-free neural net in a C or embedded codebase and do not require modern deep-learning primitives like convolutions or automatic differentiation. Skip it if you are expecting GPU acceleration, transformer blocks, or a thriving ecosystem of pre-trained models.