PyTorch on a $6 Computer (www.hackster.io)

A neural network running on the Raspberry Pi Pico 2 W—a microcontroller with just 520KB of RAM—has been successfully demonstrated using Meta's ExecuTorch, a streamlined runtime for deploying PyTorch models on edge devices. Traditionally, PyTorch requires extensive resources with a 500MB+ installation and dynamic memory management, making it unsuitable for low-capacity hardware. ExecuTorch addresses these challenges by allowing developers to prepare models on more powerful machines before deploying lightweight, memory-efficient versions. The implementation involves converting trained models into static graphs, optimizing them for embedded systems, and meticulously planning memory allocation to avoid runtime overhead. For instance, the model demonstrated is a compact neural network predicting sin(x), composed of three linear layers and a total of 337 parameters, resulting in a runtime firmware of approximately 150KB. Predictions from this network achieved an accuracy of less than 0.01 error, showcasing the effectiveness of running sophisticated AI algorithms on very limited resources. This development is significant as it opens new possibilities for deploying AI in resource-constrained environments, pushing the boundaries of what edge devices can achieve in machine learning applications.