threshold-comparator8bit

8-bit magnitude comparator. Compares two 8-bit unsigned values.

Function

compare8(A, B) -> (GT, LT, EQ)

• GT = 1 if A > B
• LT = 1 if A < B
• EQ = 1 if A = B

Exactly one output is always active.

Architecture

A[7:0]          B[7:0]
   │               │
   └───────┬───────┘
           │
    ┌──────┴──────┐
    │             │
    ▼             ▼
  ┌───┐         ┌───┐
  │GT │         │LT │   Layer 1
  │A-B│         │B-A│
  │>=1│         │>=1│
  └───┘         └───┘
    │             │
    └──────┬──────┘
           │
           ▼
        ┌─────┐
        │ EQ  │         Layer 2
        │ NOR │
        └─────┘

Uses positional weighting: treats inputs as binary numbers.

GT neuron: weights A bits positively (128,64,32,16,8,4,2,1), B bits negatively. Fires when weighted sum A - B >= 1.

LT neuron: opposite weights. Fires when B - A >= 1.

EQ neuron: NOR of GT and LT. Fires when both are zero.

Parameters

Inputs	16
Outputs	3
Neurons	3
Layers	2
Parameters	37
Magnitude	1024

Truth Table (examples)

A	B	GT	LT	EQ
0	0	0	0	1
100	50	1	0	0
50	100	0	1	0
255	255	0	0	1

Usage

from safetensors.torch import load_file
import torch

w = load_file('model.safetensors')

def compare(a, b):
    a_bits = [(a >> (7-i)) & 1 for i in range(8)]
    b_bits = [(b >> (7-i)) & 1 for i in range(8)]
    inp = torch.tensor([float(x) for x in a_bits + b_bits])
    gt = int((inp @ w['gt.weight'].T + w['gt.bias'] >= 0).item())
    lt = int((inp @ w['lt.weight'].T + w['lt.bias'] >= 0).item())
    eq = int((torch.tensor([float(gt), float(lt)]) @ w['eq.weight'].T + w['eq.bias'] >= 0).item())
    return gt, lt, eq

# compare(200, 100) = (1, 0, 0)  # 200 > 100

License

MIT