This repo contains code for exploring auto completion of spice netlists.

Quick Start

First install the dependencies (as shown below). Then follow the instructions for whatever technique you would like to try. (Use Python 3)

pip install -r requirements.txt

Graph (Attention) Neural Network

python train_node_actions.py LT1001_TA05.net

Results are stored in logs/ and can be viewed with tensorboard

K-Nearest Neighbors

Using KNN seems to be the closest analog to n-grams from NLP in the graph context. This is a simple approach where we are simply trying to use the component type with the most similar neighborhood that we have seen in the training set. Each point is represented as a list of the pins where each pin is represented as a sorted list of the other elements at the node. Points are compared using hamming (edit) distance.

The approach is still pretty simple as it just uses the closest neighbor currently (K=1).

python train_knn.py path/to/netlist path/to/netlist2 ...

python predict_knn.py model_path path/to/netlist path/to/netlist2 ...