Add threshold-mux: 2:1 multiplexer

README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-mux
+
+2:1 multiplexer. Selects between two inputs based on a select signal.
+
+## Circuit
+
+```
+    a   b   s
+    │   │   │
+    │   └───┼───┐
+    └───┬───┘   │
+        │   ┌───┘
+        ▼   ▼
+  ┌─────────────┐
+  │  a AND ¬s   │   N1: w=[1,0,-1] b=-1
+  └─────────────┘
+        │
+        │   ┌─────────────┐
+        │   │  b AND s    │   N2: w=[0,1,1] b=-2
+        │   └─────────────┘
+        │         │
+        └────┬────┘
+             ▼
+        ┌─────────┐
+        │   OR    │   w=[1,1] b=-1
+        └─────────┘
+             │
+             ▼
+           output
+```
+
+## Function
+
+MUX(a, b, s) = a if s=0, b if s=1
+
+Equivalent to: OR(AND(a, NOT(s)), AND(b, s))
+
+## Truth Table
+
+| a | b | s | out |
+|---|---|---|-----|
+| 0 | 0 | 0 | 0 |
+| 0 | 0 | 1 | 0 |
+| 0 | 1 | 0 | 0 |
+| 0 | 1 | 1 | 1 |
+| 1 | 0 | 0 | 1 |
+| 1 | 0 | 1 | 0 |
+| 1 | 1 | 0 | 1 |
+| 1 | 1 | 1 | 1 |
+
+## Architecture
+
+| Layer | Neurons | Weights | Bias |
+|-------|---------|---------|------|
+| 1 | N1 (a AND ¬s) | [1, 0, -1] | -1 |
+| 1 | N2 (b AND s) | [0, 1, 1] | -2 |
+| 2 | OR | [1, 1] | -1 |
+
+**Total: 3 neurons, 11 parameters, 2 layers**
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def mux(a, b, s):
+    inp = torch.tensor([float(a), float(b), float(s)])
+    l1 = (inp @ w['layer1.weight'].T + w['layer1.bias'] >= 0).float()
+    out = (l1 @ w['layer2.weight'].T + w['layer2.bias'] >= 0).float()
+    return int(out.item())
+
+print(mux(1, 0, 0))  # 1 (selects a)
+print(mux(1, 0, 1))  # 0 (selects b)
+```
+
+## Files
+
+```
+threshold-mux/
+├── model.safetensors
+├── model.py
+├── config.json
+└── README.md
+```
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-mux",
+  "description": "2:1 multiplexer as threshold circuit",
+  "inputs": 3,
+  "outputs": 1,
+  "neurons": 3,
+  "layers": 2,
+  "parameters": 11
+}

model.py

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+import torch
+from safetensors.torch import load_file
+
+def load_model(path='model.safetensors'):
+    return load_file(path)
+
+def mux(a, b, s, weights):
+    """2:1 Multiplexer: returns a if s=0, b if s=1"""
+    inp = torch.tensor([float(a), float(b), float(s)])
+    l1 = (inp @ weights['layer1.weight'].T + weights['layer1.bias'] >= 0).float()
+    out = (l1 @ weights['layer2.weight'].T + weights['layer2.bias'] >= 0).float()
+    return int(out.item())
+
+if __name__ == '__main__':
+    w = load_model()
+    print('MUX truth table:')
+    for a in [0, 1]:
+        for b in [0, 1]:
+            for s in [0, 1]:
+                result = mux(a, b, s, w)
+                print(f'MUX({a}, {b}, s={s}) = {result}')

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b0a8e636796b10271048c4580602c5b3225013122155b8ca5bad13e7fd3e665
+size 324