archive(cnn): move CNN v1 to cnn_v1/ subdirectory

Consolidate CNN v1 (CNNEffect) into dedicated directory:
- C++ effect: src/effects → cnn_v1/src/
- Shaders: workspaces/main/shaders/cnn → cnn_v1/shaders/
- Training: training/train_cnn.py → cnn_v1/training/
- Docs: doc/CNN*.md → cnn_v1/docs/

Updated all references:
- CMake source list
- C++ includes (relative paths: ../../cnn_v1/src/)
- Asset paths (../../cnn_v1/shaders/)
- Documentation cross-references

CNN v1 remains active in timeline. For new work, use CNN v2 with
enhanced features (7D static, storage buffer, sigmoid activation).

Tests: 34/34 passing (100%)

1 files changed, 79 insertions, 0 deletions

diff --git a/cnn_v1/docs/CNN.md b/cnn_v1/docs/CNN.md
new file mode 100644
index 0000000..2dc3362
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+# Convolutional Neural Net Shader (CNN) post-processing
+
+**Status:** ✅ Foundation implemented (single-layer, expandable to multi-pass)
+
+## Idea
+
+Have the input 3d scene be processed by a multi-layer CNN trained on the side.
+Input: some rendered scene.
+Output: 'stylized' scene with CNN post-processing.
+
+**See `doc/CNN_EFFECT.md` for implementation details, usage, and API reference.**
+
+## Shader implementation
+
+### input / output
+
+Need 1 texture buffer per CNN layer.
+Input (r,g,b,1/z) for layer 0 (render 3d scene), or output from layer N-1 for layer N.
+output: (r,g,b, alpha). Don't need the 1/z information (can be fetched from input)
+
+### size of one layer
+
+Notation:
+S: the number of input samples from layer N-1.
+Example: 3x3 input -> S = 3x3 = 9. 
+
+Each S samples is 4 values (r,g,b, w=1/z).
+
+Each sample is processed by a mat4 matrix. 4 input => 4 output.
+
+Weight matrix = S x mat4
+
+Final bias: 4 values.
+
+WGSL code example: See file CNN.shader
+
+### Layers
+
+we need 3 or 4 layer ?
+Several different shaders for each layer.
+Ping-pong for input/output texture buffer between each layers?
+
+## Implementation Status
+
+**Completed:**
+- ✅ Modular WGSL shader architecture (6 snippet files)
+- ✅ CNNEffect C++ class (single-layer rendering)
+- ✅ ShaderComposer integration (#include resolution)
+- ✅ Asset registration (7 new shader assets)
+- ✅ Test coverage (test_demo_effects.cc)
+- ✅ Placeholder identity weights for testing
+
+**Size:** ~3-4 KB shader code + ~2-4 KB weights = **5-8 KB total**
+
+**Pending:**
+- ⏳ Training script (`scripts/train_cnn.py`) to generate real weights
+- ⏳ Multi-layer rendering with ping-pong textures
+- ⏳ Weight quantization for size optimization
+
+---
+
+## Training (To Be Implemented)
+
+The layer weight/bias data are hard-coded in the shaders.
+Training workflow:
+
+1. Prepare image pairs (before: raw render, after: target style)
+2. Run `python scripts/train_cnn.py --input scene.png --target stylized.png`
+3. Script generates `cnn_weights_generated.wgsl`
+4. Rebuild: `cmake --build build -j4`
+
+**Reference:** File `CNN.py` contains training example (needs adaptation).
+
+Need a repository of reference image pairs (before/after) for training and validation.
+Each input image is randomly sampled into 3×3 patch of (r,g,b,1/z) input samples.
+And trained to match the (r,g,b,a) output.
+
+Training generates the .wgsl code for layers' shaders.
+