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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- sorting
+---
+
+# threshold-bitonic-sort
+
+4-element bitonic sorting network. Sorts single-bit elements in ascending order.
+
+## Circuit
+
+Outputs sorted bits: Y0 ≤ Y1 ≤ Y2 ≤ Y3
+
+## Parameters
+
+| | |
+|---|---|
+| Elements | 4 |
+| Neurons | 4 |
+| Parameters | 20 |
+| Magnitude | 26 |
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-bitonic-sort",
+  "description": "4-element bitonic sort network",
+  "inputs": 4,
+  "outputs": 4,
+  "neurons": 4,
+  "layers": 1,
+  "parameters": 20
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# 4-element Bitonic Sort Network (single-bit elements)
+# Sorts 4 bits into ascending order
+
+def add_neuron(name, w_list, bias):
+    weights[f'{name}.weight'] = torch.tensor([w_list], dtype=torch.float32)
+    weights[f'{name}.bias'] = torch.tensor([bias], dtype=torch.float32)
+
+# For single-bit sort, output Y[i] = 1 iff at least (i+1) input bits are 1
+# Y0 (min) = 1 iff all 4 inputs are 1 = AND
+# Y1 = 1 iff at least 3 inputs are 1
+# Y2 = 1 iff at least 2 inputs are 1
+# Y3 (max) = 1 iff at least 1 input is 1 = OR
+
+add_neuron('y0', [1.0, 1.0, 1.0, 1.0], -4.0)  # >= 4
+add_neuron('y1', [1.0, 1.0, 1.0, 1.0], -3.0)  # >= 3
+add_neuron('y2', [1.0, 1.0, 1.0, 1.0], -2.0)  # >= 2
+add_neuron('y3', [1.0, 1.0, 1.0, 1.0], -1.0)  # >= 1
+
+save_file(weights, 'model.safetensors')
+
+def bitonic_sort4(a, b, c, d):
+    count = a + b + c + d
+    # Sorted output: count 1s at top
+    y0 = 1 if count >= 4 else 0
+    y1 = 1 if count >= 3 else 0
+    y2 = 1 if count >= 2 else 0
+    y3 = 1 if count >= 1 else 0
+    return y0, y1, y2, y3
+
+print("Verifying 4-element bitonic sort (single-bit)...")
+errors = 0
+for val in range(16):
+    a, b, c, d = (val>>3)&1, (val>>2)&1, (val>>1)&1, val&1
+    y0, y1, y2, y3 = bitonic_sort4(a, b, c, d)
+
+    # Verify sorted order
+    if not (y0 <= y1 <= y2 <= y3):
+        errors += 1
+        print(f"ERROR: not sorted")
+
+    # Verify count preserved
+    if sum([a, b, c, d]) != sum([y0, y1, y2, y3]):
+        errors += 1
+
+if errors == 0:
+    print("All 16 test cases passed!")
+else:
+    print(f"FAILED: {errors} errors")
+
+mag = sum(t.abs().sum().item() for t in weights.values())
+print(f"Magnitude: {mag:.0f}")
+print(f"Parameters: {sum(t.numel() for t in weights.values())}")

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cc9a06c0ab80741d20f98131f1421546b1352b8a4db3e7d74d9128b58eee7679
+size 592