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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- encoder
+---
+
+# threshold-binarytothermometer
+
+Converts 3-bit binary to 7-bit thermometer code. A single-layer threshold circuit.
+
+## Circuit
+
+```
+       b₂      b₁      b₀
+        │       │       │
+        │       │       │
+    ┌───┴───┬───┴───┬───┴───┐
+    │       │       │       │
+    ▼       ▼       ▼       ▼
+┌──────┐┌──────┐┌──────┐┌──────┐
+│  y₀  ││  y₁  ││  y₂  ││ ...  │
+│w:4,2,1│w:4,2,1│w:4,2,1│      │
+│b: -1 ││b: -2 ││b: -3 ││      │
+└──────┘└──────┘└──────┘└──────┘
+    │       │       │       │
+    ▼       ▼       ▼       ▼
+   y₀      y₁      y₂  ... y₆
+```
+
+## Thermometer Code
+
+Thermometer encoding represents value n as n consecutive ones:
+
+| Value | Binary | Thermometer |
+|-------|--------|-------------|
+| 0 | 000 | 0000000 |
+| 1 | 001 | 1000000 |
+| 2 | 010 | 1100000 |
+| 3 | 011 | 1110000 |
+| 4 | 100 | 1111000 |
+| 5 | 101 | 1111100 |
+| 6 | 110 | 1111110 |
+| 7 | 111 | 1111111 |
+
+Like mercury rising in a thermometer - higher values fill more positions.
+
+## Mechanism
+
+Each output yᵢ fires when value > i:
+
+```
+yᵢ: (4·b₂ + 2·b₁ + 1·b₀) - (i+1) ≥ 0
+```
+
+The weights [4, 2, 1] compute the binary value. The bias sets the threshold.
+
+| Output | Bias | Fires when |
+|--------|------|------------|
+| y₀ | -1 | value ≥ 1 |
+| y₁ | -2 | value ≥ 2 |
+| y₂ | -3 | value ≥ 3 |
+| y₃ | -4 | value ≥ 4 |
+| y₄ | -5 | value ≥ 5 |
+| y₅ | -6 | value ≥ 6 |
+| y₆ | -7 | value ≥ 7 |
+
+## Why Thermometer?
+
+Thermometer codes are used in:
+
+- **DACs/ADCs**: Monotonic, glitch-free conversion
+- **Flash ADCs**: Each comparator outputs one thermometer bit
+- **Priority queues**: Natural ordering representation
+- **Neural networks**: Unary encoding preserves magnitude relationships
+
+## Single-Layer Elegance
+
+This is one of the rare multi-output functions computable in a single layer. Each output is a simple threshold on the input value - no inter-neuron dependencies.
+
+## Parameters
+
+All neurons share the same weights, only biases differ:
+
+| Component | Value |
+|-----------|-------|
+| Weights (all) | [4, 2, 1] |
+| Biases | [-1, -2, -3, -4, -5, -6, -7] |
+
+**Total: 7 neurons, 28 parameters, 1 layer**
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def binary_to_therm(b2, b1, b0):
+    inp = torch.tensor([float(b2), float(b1), float(b0)])
+    return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0)
+            for i in range(7)]
+
+# Value 5 -> thermometer with 5 ones
+therm = binary_to_therm(1, 0, 1)
+print(therm)  # [1, 1, 1, 1, 1, 0, 0]
+```
+
+## Files
+
+```
+threshold-binarytothermometer/
+├── model.safetensors
+├── model.py
+├── config.json
+└── README.md
+```
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-binarytothermometer",
+  "description": "3-bit binary to 7-bit thermometer converter",
+  "inputs": 3,
+  "outputs": 7,
+  "neurons": 7,
+  "layers": 1,
+  "parameters": 28
+}

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 binary_to_thermometer(b2, b1, b0, weights):
+    """Convert 3-bit binary to 7-bit thermometer code.
+    Returns list [y0, y1, ..., y6] where yi=1 iff value > i.
+    """
+    inp = torch.tensor([float(b2), float(b1), float(b0)])
+    outputs = []
+    for i in range(7):
+        y = int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0)
+        outputs.append(y)
+    return outputs
+
+if __name__ == '__main__':
+    w = load_model()
+    print('Binary to Thermometer Converter')
+    print('Value -> Thermometer')
+    for val in range(8):
+        b2, b1, b0 = (val >> 2) & 1, (val >> 1) & 1, val & 1
+        therm = binary_to_thermometer(b2, b1, b0, w)
+        print(f"  {val} -> {''.join(map(str, therm))}")

model.safetensors

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+size 992