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---
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
- encoding
---
# 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
```python
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
|