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README.md

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

config.json

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+{
+  "name": "threshold-fp-normalize",
+  "description": "fp-normalize 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 = {}
+
+# Minifloat Normalizer (4-bit mantissa)
+# Shifts mantissa left until MSB is 1, adjusts exponent
+
+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)
+
+# Input: M3,M2,M1,M0 (mantissa), E2,E1,E0 (exponent)
+# Detect leading zeros
+add_neuron('m3_is1', [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], -1.0)  # M3=1, no shift
+add_neuron('m2_lead', [-1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.0)  # M3=0,M2=1, shift 1
+add_neuron('m1_lead', [-1.0, -1.0, 1.0, 0.0, 0.0, 0.0, 0.0], 1.0)  # shift 2
+add_neuron('m0_lead', [-1.0, -1.0, -1.0, 1.0, 0.0, 0.0, 0.0], 2.0)  # shift 3
+
+save_file(weights, 'model.safetensors')
+
+def normalize(m3, m2, m1, m0, e2, e1, e0):
+    m = m3*8 + m2*4 + m1*2 + m0
+    e = e2*4 + e1*2 + e0
+
+    if m == 0:
+        return 0, 0, 0, 0, 0, 0, 0
+
+    shift = 0
+    while (m & 8) == 0 and shift < 4:
+        m = (m << 1) & 0xF
+        shift += 1
+
+    e = max(0, e - shift)
+    return (m>>3)&1, (m>>2)&1, (m>>1)&1, m&1, (e>>2)&1, (e>>1)&1, e&1
+
+print("Verifying FP normalize...")
+errors = 0
+for m in range(16):
+    for e in range(8):
+        m3, m2, m1, m0 = (m>>3)&1, (m>>2)&1, (m>>1)&1, m&1
+        e2, e1, e0 = (e>>2)&1, (e>>1)&1, e&1
+        result = normalize(m3, m2, m1, m0, e2, e1, e0)
+        # Verify MSB is 1 (or value is 0)
+        if m != 0 and result[0] != 1:
+            errors += 1
+
+if errors == 0:
+    print("All 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 sha256:42e79c802b7a472c1f36b1598d209bf2e5398d14a488b633da7642dfb1ebe170
+size 680