threshold-onehot-encoder

2-to-4 one-hot encoder. Converts a 2-bit binary value to a 4-bit one-hot representation.

Function

onehot_encode(a1, a0) -> (y3, y2, y1, y0)

Exactly one output bit is set, corresponding to the input value.

Truth Table

a1	a0	y3	y2	y1	y0	Value
0	0	0	0	0	1	0
0	1	0	0	1	0	1
1	0	0	1	0	0	2
1	1	1	0	0	0	3

Architecture

Single-layer implementation using threshold logic:

        a1    a0
        │     │
    ┌───┴─────┴───┐
    │             │
    ▼   ▼   ▼   ▼
  ┌───┬───┬───┬───┐
  │y3 │y2 │y1 │y0 │  Layer 1
  │AND│A·B│A·B│NOR│
  └───┴───┴───┴───┘
    │   │   │   │
    ▼   ▼   ▼   ▼

Each output is a single threshold neuron:

• y0 = NOR(a1, a0): w=[-1,-1], b=0
• y1 = NOT(a1) AND a0: w=[-1,1], b=-1
• y2 = a1 AND NOT(a0): w=[1,-1], b=-1
• y3 = a1 AND a0: w=[1,1], b=-2

Parameters

Inputs	2
Outputs	4
Neurons	4
Layers	1
Parameters	12
Magnitude	12

Usage

from safetensors.torch import load_file
import torch

w = load_file('model.safetensors')

def onehot(a1, a0):
    inp = torch.tensor([float(a1), float(a0)])
    y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
    y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
    y2 = int((inp @ w['y2.weight'].T + w['y2.bias'] >= 0).item())
    y3 = int((inp @ w['y3.weight'].T + w['y3.bias'] >= 0).item())
    return y3, y2, y1, y0

# onehot(1, 0) = (0, 1, 0, 0)  # value 2

Applications

• Address decoding
• Memory select lines
• State machine encoding
• Neural network input preprocessing

License

MIT