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17 hoursTODO: 8-bit weight quantization for 2× size reductionskal
- Add QAT (quantization-aware training) notes - Requires training with fake quantization - Target: ~1.6 KB weights (vs 3.2 KB f16) - Shader unpacking needs adaptation (4× u8 per u32)
17 hoursCNN v2: storage buffer architecture foundationskal
- Add binary weight format (header + layer info + packed f16) - New export_cnn_v2_weights.py for binary weight export - Single cnn_v2_compute.wgsl shader with storage buffer - Load weights in CNNv2Effect::load_weights() - Create layer compute pipeline with 5 bindings - Fast training config: 100 epochs, 3×3 kernels, 8→4→4 channels Next: Complete bind group creation and multi-layer compute execution