MNIST64: Interactive speed 6502 implementation on the Commodore 64 [pdf] (jarnoh.github.io)

Researchers have successfully implemented an efficient binary convolutional neural network (CNN) on the Commodore 64, achieving an impressive accuracy of 99.42% for handwritten digit recognition using the MNIST and EMNIST datasets. This novel approach leverages the 6502 CPU's limitations—namely, the absence of multiplication instructions—to adopt advanced techniques like XNOR-popcount operations and confidence-gated early exits. The system processes images with minimal latency, averaging just 0.653 seconds per image while maintaining top-tier accuracy through an adaptive cascade model that routes easier digits through a faster path. This development is significant for the AI/ML community as it demonstrates the possibility of executing sophisticated machine learning algorithms on retro hardware, suggesting that extreme quantization and optimization could allow AI methods to be deployed in constricted environments without modern accelerators. The research emphasizes the potential for binary neural networks to deliver high performance even on hardware introduced decades ago, hinting at new avenues for efficiency in AI applications. With a model footprint of only 20,852 bytes and optimizations that cut average inference time by roughly 60%, this work challenges conventional expectations regarding the computational demands of machine learning tasks.