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---
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
---
# threshold-comparator4bit
4-bit magnitude comparator. Compares two 4-bit unsigned integers.
## Function
compare(A, B) -> (GT, LT, EQ)
- GT = 1 if A > B
- LT = 1 if A < B
- EQ = 1 if A = B
## Inputs
| Input | Description |
|-------|-------------|
| A3-A0 | 4-bit number A (A3 is MSB) |
| B3-B0 | 4-bit number B (B3 is MSB) |
## Architecture
```
A3 A2 A1 A0 B3 B2 B1 B0
| | | | | | | |
+--+--+--+--+--+--+--+
| |
v v
[GT: A-B >= 1] [LT: B-A >= 1]
| |
+--------+--------+
|
v
[EQ: NOR(GT,LT)]
```
Layer 1:
- GT: weights [8, 4, 2, 1, -8, -4, -2, -1], bias -1
- LT: weights [-8, -4, -2, -1, 8, 4, 2, 1], bias -1
Layer 2:
- EQ: weights [-1, -1], bias 0 (NOR gate)
## Parameters
| | |
|---|---|
| Inputs | 8 |
| Outputs | 3 (GT, LT, EQ) |
| Neurons | 3 |
| Layers | 2 |
| Parameters | 21 |
| Magnitude | 64 |
## Usage
```python
from safetensors.torch import load_file
import torch
w = load_file('model.safetensors')
def compare4(a3, a2, a1, a0, b3, b2, b1, b0):
inp = torch.tensor([float(a3), float(a2), float(a1), float(a0),
float(b3), float(b2), float(b1), float(b0)])
gt = int((inp @ w['gt.weight'].T + w['gt.bias'] >= 0).item())
lt = int((inp @ w['lt.weight'].T + w['lt.bias'] >= 0).item())
gt_lt = torch.tensor([float(gt), float(lt)])
eq = int((gt_lt @ w['eq.weight'].T + w['eq.bias'] >= 0).item())
return gt, lt, eq
# Compare 5 vs 3
gt, lt, eq = compare4(0, 1, 0, 1, 0, 0, 1, 1)
print(f"5 vs 3: GT={gt}, LT={lt}, EQ={eq}") # GT=1, LT=0, EQ=0
```
## License
MIT
|