Four stumbling blocks, one whirlwind, and 60% on CIFAR-10 with M0+

▤ Summary

This article covers experiments with neural network architectures and quantized inference, including CNNs, ViTs, RNNs, and LSTMs. It highlights accuracy results on datasets such as MNIST, Fashion-MNIST, and CIFAR-10, and it focuses on ternary mapping, scaling, and variance handling. The piece also describes a C11 and Cortex-M0+ deployment path that aims for bit-exact behavior on embedded hardware. The main takeaway is that careful quantization and implementation choices can materially improve small-model performance on constrained devices.

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