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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-butterfly-fft
+
+butterfly-fft threshold logic implementation.
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-butterfly-fft",
+  "description": "butterfly-fft circuit",
+  "inputs": 8,
+  "outputs": 8,
+  "neurons": 8,
+  "layers": 2,
+  "parameters": 64
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# 2-point Butterfly (FFT building block)
+# Computes: Y0 = X0 + X1, Y1 = X0 - X1
+
+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 digital implementation with 4-bit inputs:
+# Input: X0[3:0], X1[3:0] (8 bits)
+# Output: Y0[4:0], Y1[4:0] (need extra bit for overflow)
+
+# Y0 = X0 + X1 (addition)
+add_neuron('y0_sum', [8.0, 4.0, 2.0, 1.0, 8.0, 4.0, 2.0, 1.0], 0.0)
+
+# Y1 = X0 - X1 (subtraction)
+add_neuron('y1_diff', [8.0, 4.0, 2.0, 1.0, -8.0, -4.0, -2.0, -1.0], 0.0)
+
+save_file(weights, 'model.safetensors')
+
+def butterfly(x0, x1):
+    return x0 + x1, x0 - x1
+
+print("Verifying butterfly (FFT)...")
+errors = 0
+for x0 in range(16):
+    for x1 in range(16):
+        y0, y1 = butterfly(x0, x1)
+        if y0 != x0 + x1 or y1 != x0 - x1:
+            errors += 1
+
+if errors == 0:
+    print("All 256 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 sha256:0d67e6174f321669ef3e0d8026b27e7f94a24640e2e8ad6b48f60378e2dc6967
+size 352