RNN-Cpp

Project Overview

RNN-Cpp is a high-performance neural network library written in C++ that's specifically designed for embedded systems and resource-constrained environments. The library provides efficient implementations of various neural network architectures with a focus on minimal memory footprint and maximum computational efficiency.

This project addresses the growing need for AI capabilities in embedded systems, where traditional deep learning frameworks are too resource-intensive. RNN-Cpp enables developers to deploy sophisticated neural network models on microcontrollers, IoT devices, and other embedded platforms.

The library includes implementations of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and other sequential models that are particularly useful for time-series prediction, natural language processing, and sensor data analysis in embedded applications.

Key Features:

• Memory-efficient design for embedded systems
• High-performance C++ implementation
• Support for RNN, LSTM, and other sequential models
• Header-only library for easy integration
• Comprehensive testing and documentation
• Production-ready code quality