threshold-onehot-decoder

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

Function

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

Input must have exactly one bit set.

Truth Table

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

Architecture

Single-layer implementation:

  y3   y2   y1   y0
   │    │    │    │
   └────┴────┴────┘
         │
    ┌────┴────┐
    │         │
    ▼         ▼
  ┌───┐     ┌───┐
  │a1 │     │a0 │  Layer 1
  │OR │     │OR │
  └───┘     └───┘
    │         │
    ▼         ▼

Each output is a single OR gate:

• a0 = OR(y1, y3): w=[1,0,1,0], b=-1
• a1 = OR(y2, y3): w=[1,1,0,0], b=-1

The decoder recognizes that:

• Bit 0 of position = 1 for positions 1 and 3
• Bit 1 of position = 1 for positions 2 and 3

Parameters

Inputs	4
Outputs	2
Neurons	2
Layers	1
Parameters	10
Magnitude	6

Usage

from safetensors.torch import load_file
import torch

w = load_file('model.safetensors')

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

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

Applications

• Priority encoder output processing
• State machine decoding
• Memory address reconstruction
• Neural network output interpretation

License

MIT