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---
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
---
# threshold-buffer
4-bit buffer (identity function). Passes input through unchanged.
## Function
buffer4(x3, x2, x1, x0) -> (y3, y2, y1, y0)
Output equals input: y_i = x_i for all i.
## Truth Table
| Input | Output |
|-------|--------|
| 0000 | 0000 |
| 0001 | 0001 |
| ... | ... |
| 1111 | 1111 |
## Architecture
Single-layer, each output independently buffers one input:
```
x3 x2 x1 x0
│ │ │ │
â–¼ â–¼ â–¼ â–¼
â—‹ â—‹ â—‹ â—‹ Layer 1
│ │ │ │
â–¼ â–¼ â–¼ â–¼
y3 y2 y1 y0
```
Each neuron: w=[...,1,...], b=-1 (single input with weight 1).
## Parameters
| | |
|---|---|
| Inputs | 4 |
| Outputs | 4 |
| Neurons | 4 |
| Layers | 1 |
| Parameters | 20 |
| Magnitude | 8 |
## Purpose
While trivial, buffers serve several purposes:
- Signal regeneration in long chains
- Fan-out amplification
- Timing alignment
- Isolation between circuit stages
## Usage
```python
from safetensors.torch import load_file
import torch
w = load_file('model.safetensors')
def buffer(x3, x2, x1, x0):
inp = torch.tensor([float(x3), float(x2), float(x1), float(x0)])
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
```
## License
MIT
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