I ran MNIST on an ESP32-C3 without TensorFlow, TFLite, or any ML runtime
Alex Rosito successfully ran the MNIST digit recognition task on an ESP32-C3 without using TensorFlow or any machine learning runtime. The project evolved from a simple experiment into a minimal neural network toolchain designed for edge inference under constraints. It aims to provide a lightweight solution for deploying neural networks in environments with limited resources.
- ▪The neural network was compiled directly into a C header and executed as firmware.
- ▪The project includes a minimal neural network training engine, a model exporter, and a CLI runtime for cross-platform execution.
- ▪The focus is on creating a usable neural inference stack that is practical and does not rely on heavy dependencies.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3903394) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Alex Rosito Posted on May 20 I ran MNIST on an ESP32-C3 without TensorFlow, TFLite, or any ML runtime #cpp #embeddedsystems #arduino #esp32 I ran MNIST digit recognition on an ESP32-C3 — without TensorFlow, TFLite, or any ML runtime. The neural network is compiled directly into a C header and executed as firmware. From Perceptrons to a Cross-Platform NN CLI for Edge Inference It started, like many questionable engineering projects, with curiosity about a perceptron.
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