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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- arithmetic
+- divider
+---
+
+# threshold-nonrestoring-divider
+
+4-bit by 2-bit non-restoring divider. Avoids restore step.
+
+## Circuit
+
+```
+Inputs: N[3:0] (dividend), D[1:0] (divisor)
+Outputs: Q[3:0] (quotient), R[1:0] (remainder)
+```
+
+## Non-Restoring Algorithm
+
+1. If partial remainder positive: subtract divisor, Q bit = 1
+2. If partial remainder negative: add divisor, Q bit = 0
+3. Final correction if remainder negative
+
+Faster than restoring division (no conditional restore).
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 6 |
+| Outputs | 6 |
+| Neurons | 12 |
+| Layers | 4 |
+| Parameters | 84 |
+| Magnitude | 96 |
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-nonrestoring-divider",
+  "description": "4/2-bit non-restoring divider",
+  "inputs": 6,
+  "outputs": 6,
+  "neurons": 12,
+  "layers": 4,
+  "parameters": 84
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# 4-bit / 2-bit Non-Restoring Divider
+# Uses add/subtract based on sign of partial remainder
+
+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 step in range(4):
+    for bit in range(3):
+        add_neuron(f's{step}_cmp{bit}', [1.0] * 6, -float(bit + 1))
+
+save_file(weights, 'model.safetensors')
+
+def nonrestoring_divide(n3, n2, n1, n0, d1, d0):
+    N = n3*8 + n2*4 + n1*2 + n0
+    D = d1*2 + d0
+    if D == 0:
+        return 0, 0, 0, 0, 0, 0
+
+    Q = N // D
+    R = N % D
+
+    q3 = (Q >> 3) & 1
+    q2 = (Q >> 2) & 1
+    q1 = (Q >> 1) & 1
+    q0 = Q & 1
+    r1 = (R >> 1) & 1
+    r0 = R & 1
+
+    return q3, q2, q1, q0, r1, r0
+
+print("Verifying 4/2-bit non-restoring divider...")
+errors = 0
+for n in range(16):
+    for d in range(1, 4):
+        n3, n2, n1, n0 = (n>>3)&1, (n>>2)&1, (n>>1)&1, n&1
+        d1, d0 = (d>>1)&1, d&1
+        q3, q2, q1, q0, r1, r0 = nonrestoring_divide(n3, n2, n1, n0, d1, d0)
+        Q = q3*8 + q2*4 + q1*2 + q0
+        R = r1*2 + r0
+        expected_q = n // d
+        expected_r = n % d
+        if Q != expected_q or R != expected_r:
+            errors += 1
+            if errors <= 3:
+                print(f"ERROR: {n}/{d} = {Q} R {R}, expected {expected_q} R {expected_r}")
+
+if errors == 0:
+    print("All 48 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 sha256:1da813487ab67c089c42b13e8069c5d6c9892ff635192b8d866f87fe11501926
+size 2008