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
│    │    │    │
▼    ▼    ▼    ▼
○    ○    ○    ○   Layer 1
│    │    │    │
▼    ▼    ▼    ▼
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

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