threshold-priorityencoder4

4-to-2 priority encoder. Outputs binary encoding of highest-priority active input.

Function

priority_encode(i3, i2, i1, i0) -> (y1, y0, valid)

• i3 = highest priority, i0 = lowest priority
• y1,y0 = 2-bit binary encoding of highest active input
• valid = 1 if any input is active

Truth Table (selected)

i3	i2	i1	i0	y1	y0	v	highest
0	0	0	0	0	0	0	none
0	0	0	1	0	0	1	i0
0	0	1	X	0	1	1	i1
0	1	X	X	1	0	1	i2
1	X	X	X	1	1	1	i3

Architecture

Single layer with 3 neurons:

• y1 = i3 OR i2: weights [1,1,0,0], bias -1
• y0 = i3 OR (i1 AND NOT i2): weights [2,-1,1,0], bias -1
• v = i3 OR i2 OR i1 OR i0: weights [1,1,1,1], bias -1

Parameters

Inputs	4
Outputs	3
Neurons	3
Layers	1
Parameters	15
Magnitude	13

Usage

from safetensors.torch import load_file
import torch

w = load_file('model.safetensors')

def priority_encode(i3, i2, i1, i0):
    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
    y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
    y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
    v = int((inp @ w['v.weight'].T + w['v.bias'] >= 0).item())
    return y1, y0, v

print(priority_encode(0, 1, 1, 0))  # (1, 0, 1) -> i2 is highest
print(priority_encode(1, 1, 1, 1))  # (1, 1, 1) -> i3 is highest

License

MIT