Implement the **forward pass of a binary perceptron**, which maps an input feature vector to a predicted class using a weighted sum and a step activation. This checks your understanding of basic neural network computation in deep learning fundamentals.

## Requirements

Implement the function
```python
def perceptron_forward(x, w, b):
    \"""
    Computes the forward pass of a binary perceptron.

    The perceptron computes:
        z = w · x + b
        y_hat = 1 if z >= 0 else 0

    Args:
        x (list[float]): Input features of length d.
        w (list[float]): Weights of length d.
        b (float): Bias term.

    Returns:
        int: Predicted label (0 or 1).
    \"""
    # Your solution below
```

**Rules:**
- Compute the logit using the formula $$z = \sum_{i=1}^{d} w_i x_i + b$$.
- Return `1` if `z >= 0`, otherwise return `0`.
- Use only NumPy and/or Python built-in libraries (no ML frameworks).
- Do not use any prebuilt perceptron or classifier utilities.

## Example

```python
x = [0.5, -1.0, 2.0]
w = [1.0, 0.5, -0.5]
b = 0.1

perceptron_forward(x, w, b)
```

Output:
```python
0
```

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Perceptron forward pass

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Argument	Type
b	float
w	np.ndarray
x	np.ndarray

1479. Perceptron forward pass

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