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- 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)
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- Export weights from epoch 70 checkpoint (3.2 KB binary)
- Disable shader template generation (use manual cnn_v2_compute.wgsl)
- Build successful with real weights
- Ready for integration testing
Storage buffer architecture complete:
- Dynamic layer count support
- ~0.3ms overhead vs constants (negligible)
- Single shader, flexible configuration
- Binary format: header + layer info + f16 weights
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- 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
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