Add 2-to-4 one-hot encoder threshold circuit

4 neurons, 1 layer, 12 parameters, magnitude 12.

.gitattributes

@@ -0,0 +1 @@
+*.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,93 @@
+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- encoding
+---
+
+# threshold-onehot-encoder
+
+2-to-4 one-hot encoder. Converts a 2-bit binary value to a 4-bit one-hot representation.
+
+## Function
+
+onehot_encode(a1, a0) -> (y3, y2, y1, y0)
+
+Exactly one output bit is set, corresponding to the input value.
+
+## Truth Table
+
+| a1 | a0 | y3 | y2 | y1 | y0 | Value |
+|----|----|----|----|----|-----|-------|
+| 0 | 0 | 0 | 0 | 0 | 1 | 0 |
+| 0 | 1 | 0 | 0 | 1 | 0 | 1 |
+| 1 | 0 | 0 | 1 | 0 | 0 | 2 |
+| 1 | 1 | 1 | 0 | 0 | 0 | 3 |
+
+## Architecture
+
+Single-layer implementation using threshold logic:
+
+```
+        a1    a0
+        │     │
+    ┌───┴─────┴───┐
+    │             │
+    ▼   ▼   ▼   ▼
+  ┌───┬───┬───┬───┐
+  │y3 │y2 │y1 │y0 │  Layer 1
+  │AND│A·B│A·B│NOR│
+  └───┴───┴───┴───┘
+    │   │   │   │
+    ▼   ▼   ▼   ▼
+```
+
+Each output is a single threshold neuron:
+- y0 = NOR(a1, a0): w=[-1,-1], b=0
+- y1 = NOT(a1) AND a0: w=[-1,1], b=-1
+- y2 = a1 AND NOT(a0): w=[1,-1], b=-1
+- y3 = a1 AND a0: w=[1,1], b=-2
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 2 |
+| Outputs | 4 |
+| Neurons | 4 |
+| Layers | 1 |
+| Parameters | 12 |
+| Magnitude | 12 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def onehot(a1, a0):
+    inp = torch.tensor([float(a1), float(a0)])
+    y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
+    y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
+    y2 = int((inp @ w['y2.weight'].T + w['y2.bias'] >= 0).item())
+    y3 = int((inp @ w['y3.weight'].T + w['y3.bias'] >= 0).item())
+    return y3, y2, y1, y0
+
+# onehot(1, 0) = (0, 1, 0, 0)  # value 2
+```
+
+## Applications
+
+- Address decoding
+- Memory select lines
+- State machine encoding
+- Neural network input preprocessing
+
+## License
+
+MIT

config.json

@@ -0,0 +1,9 @@
+{
+  "name": "threshold-onehot-encoder",
+  "description": "2-to-4 one-hot encoder",
+  "inputs": 2,
+  "outputs": 4,
+  "neurons": 4,
+  "layers": 1,
+  "parameters": 12
+}

create_safetensors.py

@@ -0,0 +1,85 @@
+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# One-Hot Encoder (2-to-4)
+# Inputs: a1, a0 (binary value 0-3)
+# Outputs: y3, y2, y1, y0 (one-hot encoding)
+#
+# a1 a0 | y3 y2 y1 y0
+# ------+------------
+#  0  0 |  0  0  0  1
+#  0  1 |  0  0  1  0
+#  1  0 |  0  1  0  0
+#  1  1 |  1  0  0  0
+#
+# Single layer implementation:
+# y0 = NOR(a1, a0) = 1 iff a1 + a0 = 0
+# y1 = NOT(a1) AND a0 = 1 iff a0 - a1 >= 1
+# y2 = a1 AND NOT(a0) = 1 iff a1 - a0 >= 1
+# y3 = a1 AND a0 = 1 iff a1 + a0 >= 2
+
+# y0 = NOR(a1, a0): neither input is 1
+weights['y0.weight'] = torch.tensor([[-1.0, -1.0]], dtype=torch.float32)
+weights['y0.bias'] = torch.tensor([0.0], dtype=torch.float32)
+
+# y1 = NOT(a1) AND a0: a0 is 1 but a1 is 0
+weights['y1.weight'] = torch.tensor([[-1.0, 1.0]], dtype=torch.float32)
+weights['y1.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+
+# y2 = a1 AND NOT(a0): a1 is 1 but a0 is 0
+weights['y2.weight'] = torch.tensor([[1.0, -1.0]], dtype=torch.float32)
+weights['y2.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+
+# y3 = a1 AND a0: both inputs are 1
+weights['y3.weight'] = torch.tensor([[1.0, 1.0]], dtype=torch.float32)
+weights['y3.bias'] = torch.tensor([-2.0], dtype=torch.float32)
+
+save_file(weights, 'model.safetensors')
+
+def onehot_encode(a1, a0):
+    inp = torch.tensor([float(a1), float(a0)])
+
+    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
+    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
+    y2 = int((inp @ weights['y2.weight'].T + weights['y2.bias'] >= 0).item())
+    y3 = int((inp @ weights['y3.weight'].T + weights['y3.bias'] >= 0).item())
+
+    return y3, y2, y1, y0
+
+def reference_onehot(a1, a0):
+    val = a1 * 2 + a0
+    return (1 if val == 3 else 0,
+            1 if val == 2 else 0,
+            1 if val == 1 else 0,
+            1 if val == 0 else 0)
+
+print("Verifying One-Hot Encoder (2-to-4)...")
+errors = 0
+for a1 in range(2):
+    for a0 in range(2):
+        result = onehot_encode(a1, a0)
+        expected = reference_onehot(a1, a0)
+        if result != expected:
+            errors += 1
+            print(f"ERROR: ({a1},{a0}) -> {result}, expected {expected}")
+
+if errors == 0:
+    print("All 4 test cases passed!")
+else:
+    print(f"FAILED: {errors} errors")
+
+print("\nTruth Table:")
+print("a1 a0 | y3 y2 y1 y0 | value")
+print("-" * 30)
+for a1 in range(2):
+    for a0 in range(2):
+        y3, y2, y1, y0 = onehot_encode(a1, a0)
+        val = a1 * 2 + a0
+        print(f" {a1}  {a0}  |  {y3}  {y2}  {y1}  {y0}  |   {val}")
+
+mag = sum(t.abs().sum().item() for t in weights.values())
+print(f"\nMagnitude: {mag:.0f}")
+print(f"Parameters: {sum(t.numel() for t in weights.values())}")
+print(f"Neurons: {len([k for k in weights.keys() if 'weight' in k])}")

model.py

@@ -0,0 +1,24 @@
+import torch
+from safetensors.torch import load_file
+
+def load_model(path='model.safetensors'):
+    return load_file(path)
+
+def onehot_encode(a1, a0, weights):
+    """Convert 2-bit binary to 4-bit one-hot encoding."""
+    inp = torch.tensor([float(a1), float(a0)])
+
+    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
+    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
+    y2 = int((inp @ weights['y2.weight'].T + weights['y2.bias'] >= 0).item())
+    y3 = int((inp @ weights['y3.weight'].T + weights['y3.bias'] >= 0).item())
+
+    return y3, y2, y1, y0
+
+if __name__ == '__main__':
+    w = load_model()
+    print('One-Hot Encoder (2-to-4):')
+    for val in range(4):
+        a1, a0 = (val >> 1) & 1, val & 1
+        y3, y2, y1, y0 = onehot_encode(a1, a0, w)
+        print(f'  {val} ({a1}{a0}) -> {y3}{y2}{y1}{y0}')

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b68212cf121754735cc150c45cdb9587afe702f91a23356c4f210e00ecf60fd5
+size 560