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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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--- |
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# threshold-reverse4 |
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4-bit bit reversal. Reverses the order of bits. |
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## Function |
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reverse4(a3, a2, a1, a0) = [a0, a1, a2, a3] |
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| Input | Output | |
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|-------|--------| |
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| 0001 | 1000 | |
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| 1000 | 0001 | |
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| 0110 | 0110 | |
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| 1010 | 0101 | |
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## Architecture |
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Single layer with 4 neurons, each copying one input bit to its reversed position. |
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| Output | Copies from | Weights [a3,a2,a1,a0] | Bias | |
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|--------|-------------|------------------------|------| |
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| y3 | a0 | [0, 0, 0, 1] | -1 | |
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| y2 | a1 | [0, 0, 1, 0] | -1 | |
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| y1 | a2 | [0, 1, 0, 0] | -1 | |
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| y0 | a3 | [1, 0, 0, 0] | -1 | |
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## Parameters |
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| | | |
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|---|---| |
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| Inputs | 4 | |
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| Outputs | 4 | |
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| Neurons | 4 | |
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| Layers | 1 | |
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| Parameters | 8 | |
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| Magnitude | 8 | |
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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 reverse4(a3, a2, a1, a0): |
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inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)]) |
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return [int((inp @ w[f'y{i}.weight'].T + w[f'y{i}.bias'] >= 0).item()) |
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for i in [3, 2, 1, 0]] |
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print(reverse4(1, 0, 0, 0)) # [0, 0, 0, 1] |
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``` |
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## License |
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MIT |
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