Researchers demonstrated a binarized neural network achieving 99.42% accuracy on handwritten digit recognition running on a stock 1975 Commodore 64 with 64 kB RAM and no multiply instruction. The model uses extreme quantization and confidence-gated early exit to reduce inference time by 60% while maintaining accuracy on real hardware.