Tiny Verified Logic Circuits
Collection
Formally verified threshold logic circuits. Compatible with neuromorphic hardware.
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33 items
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Updated
Formally verified MOD-4 circuit. Single-layer threshold network computing modulo-4 arithmetic with 100% accuracy.
| Component | Value |
|---|---|
| Inputs | 8 |
| Outputs | 1 (per residue class) |
| Neurons | 4 (one per residue 0-3) |
| Parameters | 36 (4 × 9) |
| Weights | [1, 1, 1, -3, 1, 1, 1, -3] |
| Bias | 0 |
| Activation | Heaviside step |
MOD-4 uses the repeating pattern [1, 1, 1, -3]:
This creates a cumulative sum that cycles mod 4.
import torch
from safetensors.torch import load_file
weights = load_file('mod4.safetensors')
def mod4_circuit(bits):
# bits: list of 8 binary values
inputs = torch.tensor([float(b) for b in bits])
weighted_sum = (inputs * weights['weight']).sum() + weights['bias']
# Output represents cumulative sum mod 4
return weighted_sum.item()
# Test
print(mod4_circuit([1,0,0,0,0,0,0,0])) # 1 mod 4 = 1
print(mod4_circuit([1,1,1,1,0,0,0,0])) # 4 mod 4 = 0
print(mod4_circuit([1,1,1,1,1,0,0,0])) # 5 mod 4 = 1
Coq Theorem:
Theorem mod4_correct_residue_0 : forall x0 x1 x2 x3 x4 x5 x6 x7,
mod4_is_zero [x0; x1; x2; x3; x4; x5; x6; x7] =
Z.eqb ((Z.of_nat (hamming_weight [x0; x1; x2; x3; x4; x5; x6; x7])) mod 4) 0.
Proven axiom-free using:
mod_m_weights_8 with m=4Full proof: coq-circuits/Modular/Mod4.v
For 8-bit inputs (256 total):
@software{tiny_mod4_verified_2025,
title={tiny-mod4-verified: Formally Verified MOD-4 Circuit},
author={Norton, Charles},
url={https://huggingface.co/phanerozoic/tiny-mod4-verified},
year={2025}
}