4-bit right shift, magnitude 7

README.md

@@ -0,0 +1,53 @@
+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-shiftright4
+
+4-bit logical right shift by 1 bit.
+
+## Function
+
+shiftright4(a3, a2, a1, a0) = [0, a3, a2, a1]
+
+LSB (a0) is discarded, 0 shifts in at MSB.
+
+## Examples
+
+| Input | Output |
+|-------|--------|
+| 0001 | 0000 |
+| 0101 | 0010 |
+| 1000 | 0100 |
+| 1111 | 0111 |
+
+## Architecture
+
+Single layer with 4 neurons.
+
+| Output | Source | Weights | Bias |
+|--------|--------|---------|------|
+| y3 | 0 | [0,0,0,0] | -1 |
+| y2 | a3 | [1,0,0,0] | -1 |
+| y1 | a2 | [0,1,0,0] | -1 |
+| y0 | a1 | [0,0,1,0] | -1 |
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 4 |
+| Outputs | 4 |
+| Neurons | 4 |
+| Layers | 1 |
+| Parameters | 8 |
+| Magnitude | 7 |
+
+## License
+
+MIT

config.json

@@ -0,0 +1,9 @@
+{
+  "name": "threshold-shiftright4",
+  "description": "4-bit right shift by 1",
+  "inputs": 4,
+  "outputs": 4,
+  "neurons": 4,
+  "layers": 1,
+  "parameters": 8
+}

create_safetensors.py

@@ -0,0 +1,30 @@
+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# Input: [a3, a2, a1, a0], Output: [0, a3, a2, a1]
+weights['y3.weight'] = torch.tensor([[0.0, 0.0, 0.0, 0.0]], dtype=torch.float32)
+weights['y3.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+weights['y2.weight'] = torch.tensor([[1.0, 0.0, 0.0, 0.0]], dtype=torch.float32)
+weights['y2.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+weights['y1.weight'] = torch.tensor([[0.0, 1.0, 0.0, 0.0]], dtype=torch.float32)
+weights['y1.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+weights['y0.weight'] = torch.tensor([[0.0, 0.0, 1.0, 0.0]], dtype=torch.float32)
+weights['y0.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+
+save_file(weights, 'model.safetensors')
+
+print("Verifying shiftright4...")
+errors = 0
+for i in range(16):
+    a3, a2, a1, a0 = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
+    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
+    result = [int((inp @ weights[f'y{j}.weight'].T + weights[f'y{j}.bias'] >= 0).item()) for j in [3,2,1,0]]
+    expected = [0, a3, a2, a1]
+    if result != expected:
+        errors += 1
+        print(f"ERROR: {a3}{a2}{a1}{a0} -> {result}, expected {expected}")
+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

@@ -0,0 +1,12 @@
+import torch
+from safetensors.torch import load_file
+def load_model(path='model.safetensors'):
+    return load_file(path)
+def shiftright4(a3, a2, a1, a0, w):
+    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
+    return [int((inp @ w[f'y{i}.weight'].T + w[f'y{i}.bias'] >= 0).item()) for i in [3,2,1,0]]
+if __name__ == '__main__':
+    w = load_model()
+    for val in [0b0001, 0b0101, 0b1000, 0b1111]:
+        a3, a2, a1, a0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
+        print(f'{a3}{a2}{a1}{a0} >> 1 = {"".join(map(str, shiftright4(a3, a2, a1, a0, w)))}')