| | ---
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| | license: mit
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| | tags:
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| | - pytorch
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| | - safetensors
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| | - threshold-logic
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| | - neuromorphic
|
| | ---
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| |
|
| | # threshold-parity8
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| |
|
| | 8-bit parity function. Outputs 1 if odd number of inputs are high.
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| |
|
| | ## Function
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| |
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| | parity8(b0..b7) = b0 XOR b1 XOR b2 XOR b3 XOR b4 XOR b5 XOR b6 XOR b7
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| |
|
| | ## Architecture
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| |
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| | Balanced tree of 7 XOR2 gates:
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| |
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| | ```
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| | b0 b1 b2 b3 b4 b5 b6 b7
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| | \/ \/ \/ \/
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| | xor01 xor23 xor45 xor67 (Level 1: 4 gates)
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| | \ / \ /
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| | xor0123 xor4567 (Level 2: 2 gates)
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| | \ /
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| | xor_final (Level 3: 1 gate)
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| | ```
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| |
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| | Each XOR2 uses OR-NAND-AND structure (3 neurons, 9 params, magnitude 10).
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| |
|
| | ## Parameters
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| |
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| | | | |
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| | |---|---|
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| | | Inputs | 8 |
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| | | Outputs | 1 |
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| | | Neurons | 21 |
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| | | Layers | 6 |
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| | | Parameters | 63 |
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| | | Magnitude | 70 |
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| |
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| | ## Usage
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| |
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| | ```python
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| | from safetensors.torch import load_file
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| |
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| | w = load_file('model.safetensors')
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| |
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| | def xor2(a, b, prefix):
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| | or_out = int(a * w[f'{prefix}.or.weight'][0] + b * w[f'{prefix}.or.weight'][1] + w[f'{prefix}.or.bias'] >= 0)
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| | nand_out = int(a * w[f'{prefix}.nand.weight'][0] + b * w[f'{prefix}.nand.weight'][1] + w[f'{prefix}.nand.bias'] >= 0)
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| | return int(or_out * w[f'{prefix}.and.weight'][0] + nand_out * w[f'{prefix}.and.weight'][1] + w[f'{prefix}.and.bias'] >= 0)
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| |
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| | def parity8(bits):
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| | x01 = xor2(bits[0], bits[1], 'xor_01')
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| | x23 = xor2(bits[2], bits[3], 'xor_23')
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| | x45 = xor2(bits[4], bits[5], 'xor_45')
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| | x67 = xor2(bits[6], bits[7], 'xor_67')
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| | x0123 = xor2(x01, x23, 'xor_0123')
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| | x4567 = xor2(x45, x67, 'xor_4567')
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| | return xor2(x0123, x4567, 'xor_final')
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| |
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| | print(parity8([1, 0, 1, 0, 1, 0, 1, 0])) # 0 (even)
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| | print(parity8([1, 1, 1, 0, 0, 0, 0, 0])) # 1 (odd)
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| | ```
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| |
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| | ## License
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| |
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| | MIT
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| |
|
