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--- |
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license: mit |
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tags: |
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- pytorch |
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- safetensors |
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- threshold-logic |
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- neuromorphic |
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- encoder |
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--- |
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# threshold-priorityencoder4 |
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4-to-2 priority encoder. Outputs binary encoding of highest-priority active input. |
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## Function |
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priority_encode(i3, i2, i1, i0) -> (y1, y0, valid) |
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- i3 = highest priority, i0 = lowest priority |
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- y1,y0 = 2-bit binary encoding of highest active input |
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- valid = 1 if any input is active |
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## Truth Table (selected) |
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| i3 | i2 | i1 | i0 | y1 | y0 | v | highest | |
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|----|----|----|----|----|----|----|---------| |
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| 0 | 0 | 0 | 0 | 0 | 0 | 0 | none | |
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| 0 | 0 | 0 | 1 | 0 | 0 | 1 | i0 | |
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| 0 | 0 | 1 | X | 0 | 1 | 1 | i1 | |
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| 0 | 1 | X | X | 1 | 0 | 1 | i2 | |
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| 1 | X | X | X | 1 | 1 | 1 | i3 | |
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## Architecture |
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Single layer with 3 neurons: |
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- y1 = i3 OR i2: weights [1,1,0,0], bias -1 |
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- y0 = i3 OR (i1 AND NOT i2): weights [2,-1,1,0], bias -1 |
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- v = i3 OR i2 OR i1 OR i0: weights [1,1,1,1], bias -1 |
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## Parameters |
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|---|---| |
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| Inputs | 4 | |
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| Outputs | 3 | |
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| Neurons | 3 | |
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| Layers | 1 | |
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| Parameters | 15 | |
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| Magnitude | 13 | |
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## Usage |
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```python |
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from safetensors.torch import load_file |
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import torch |
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w = load_file('model.safetensors') |
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def priority_encode(i3, i2, i1, i0): |
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inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)]) |
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y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item()) |
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y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item()) |
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v = int((inp @ w['v.weight'].T + w['v.bias'] >= 0).item()) |
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return y1, y0, v |
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print(priority_encode(0, 1, 1, 0)) # (1, 0, 1) -> i2 is highest |
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print(priority_encode(1, 1, 1, 1)) # (1, 1, 1) -> i3 is highest |
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``` |
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## License |
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MIT |
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