4-bit zero detector, magnitude 4

README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-iszero4
+
+4-bit zero detector. Outputs 1 if all 4 input bits are zero.
+
+## Function
+
+iszero4(a, b, c, d) = 1 if (a + b + c + d) == 0, else 0
+
+Equivalent to 4-input NOR gate.
+
+## Architecture
+
+Single neuron: weights [-1, -1, -1, -1], bias 0
+
+Fires when: -a - b - c - d + 0 >= 0, i.e., all inputs are 0.
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 4 |
+| Outputs | 1 |
+| Neurons | 1 |
+| Layers | 1 |
+| Parameters | 5 |
+| Magnitude | 4 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def iszero4(a, b, c, d):
+    inp = torch.tensor([float(a), float(b), float(c), float(d)])
+    return int((inp @ w['neuron.weight'].T + w['neuron.bias'] >= 0).item())
+
+print(iszero4(0, 0, 0, 0))  # 1 (is zero)
+print(iszero4(0, 0, 0, 1))  # 0 (not zero)
+```
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-iszero4",
+  "description": "4-bit zero detector",
+  "inputs": 4,
+  "outputs": 1,
+  "neurons": 1,
+  "layers": 1,
+  "parameters": 5
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+# iszero4: outputs 1 if all 4 bits are 0 (4-input NOR)
+weights = {
+    'neuron.weight': torch.tensor([[-1.0, -1.0, -1.0, -1.0]], dtype=torch.float32),
+    'neuron.bias': torch.tensor([0.0], dtype=torch.float32)
+}
+save_file(weights, 'model.safetensors')
+
+def iszero4(a, b, c, d):
+    inp = torch.tensor([float(a), float(b), float(c), float(d)])
+    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
+
+print("Verifying iszero4...")
+errors = 0
+for i in range(16):
+    bits = [(i >> j) & 1 for j in range(4)]
+    result = iszero4(*bits)
+    expected = 1 if i == 0 else 0
+    if result != expected:
+        errors += 1
+if errors == 0:
+    print("All 16 test cases passed!")
+print(f"Magnitude: {sum(t.abs().sum().item() for t in weights.values()):.0f}")

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 iszero4(a, b, c, d, weights):
+    inp = torch.tensor([float(a), float(b), float(c), float(d)])
+    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
+
+if __name__ == '__main__':
+    w = load_model()
+    print('iszero4: outputs 1 only for input 0000')
+    for i in [0, 1, 8, 15]:
+        bits = [(i >> j) & 1 for j in range(4)]
+        print(f'  {i:04b} -> {iszero4(*bits, w)}')