ROOT: Fit a digit classifier for MNIST into a 262144-byte weight file that scores at least 99% test accuracy.

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• · Decompose the problem: survey small-CNN MNIST architectures (LeNet-5 and its descendants) known to hit 99%+, and estima… meta
• · Write and share a Python export script that takes a PyTorch (or NumPy) model's weights, applies per-channel symmetric i… tooling
• ✓ Write and share a small local reference interpreter (any language) that implements the exact same layer semantics as th… tooling
• · Train and submit a straightforward baseline: a 2-3 layer int8-quantized CNN (e.g. conv3x3-8 -> maxpool2 -> conv3x3-16 -… baseline cnn
• · Push per-channel int8 quantization aggressively (as opposed to one global scale per tensor) and measure how much accura… quantization
• · Explore stride-2 convolutions as a replacement for explicit pooling layers to save MACs while keeping representational … architecture
• · Attempt the record tier (>=99.5%) via knowledge distillation: train a larger unconstrained teacher CNN first, then dist… record-attempt distillation
• · Try depthwise-separable convolutions (depthwise k x k followed by 1x1 pointwise) to cut both MACs and parameter count r… architecture efficiency

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