metadata
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
- sequential
- flipflop
threshold-jk-flipflop
JK flip-flop next-state logic as threshold circuit. Most versatile flip-flop with Set, Reset, Hold, and Toggle modes.
Circuit
J ──┐
K ──┼──► JK-FF ──┬──► Q
Q_prev ──┘ └──► Qn
Truth Table
| J | K | Q_prev | Q | Qn | Mode |
|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 1 | Hold |
| 0 | 0 | 1 | 1 | 0 | Hold |
| 0 | 1 | X | 0 | 1 | Reset |
| 1 | 0 | X | 1 | 0 | Set |
| 1 | 1 | 0 | 1 | 0 | Toggle |
| 1 | 1 | 1 | 0 | 1 | Toggle |
Modes
J=0, K=0: Hold - Q stays same
J=0, K=1: Reset - Q becomes 0
J=1, K=0: Set - Q becomes 1
J=1, K=1: Toggle - Q flips
Logic
Q = J·~Q_prev + ~K·Q_prev
Qn = ~J·~Q_prev + K·Q_prev
Architecture
| Component | Neurons |
|---|---|
| Input inversions | 4 |
| AND gates | 4 |
| OR outputs | 2 |
| Passthroughs | 2 |
Total: 12 neurons, 42 parameters, 3 layers
Advantage Over SR
Unlike SR flip-flop, JK has no invalid state. When both inputs are high, it toggles instead of producing undefined behavior.
Usage
from safetensors.torch import load_file
w = load_file('model.safetensors')
# Counter using toggle mode:
q = 0
for _ in range(4):
q_next = compute(j=1, k=1, q_prev=q, w)
q = q_next # q toggles: 0,1,0,1,...
Files
threshold-jk-flipflop/
├── model.safetensors
├── create_safetensors.py
├── config.json
└── README.md
License
MIT