GNN4REL

Graph Neural Networks for Predicting Circuit Reliability Degradation. TCAD 2022, paper

Contact Lilas Alrahis (lma387@nyu.edu)

Overview

We employ graph neural networks (GNNs) to accurately estimate the impact of process variations and device aging on the delay of any path within a circuit. GNN4REL empowers designers to perform rapid and accurate reliability estimations without accessing transistor models, standard-cell libraries, or even STA; these components are all incorporated into the GNN model via training by the foundry.

About This Repo

This repo contains the source code of the netlist-to-subgraphs transformation from our paper (TCAD '22, see the Citation Section). The scripts released here parse the designs in technology-mapped Netlist format (FinFET 14nm). Please contact Lilas Alrahis (lma387@nyu.edu) if you wish to expand Verilog netlists parsing to handle different tech libs. We release the benchmarks and STA measurements to replicate the results presented in Fig.14 in our paper, i.e., predicting the average process-variation-induced degradation.

Conversion to Graphs

Benchmarks

The ./perl_codes directory contains the adder, multiplier, max, b15, and b17 synthesized netlists used in (TCAD'22).

Scripts

The following scripts are required for the conversion:
./perl_codes/TheCircuit.pm: a Perl module we create to ease circuit's parsing. This module is required by our parser ./perl_codes/netlist_to_subgraph_directed.pl

./perl_codes/netlist_to_subgraph_directed.pl: a Perl script that reads one synthesized gate-level netlist at a time (or a number of netlists) in a given dataset and converts the dataset into a single graph. It assigns unique numerical IDs (0 to N-1) to the nodes (gates). N represents the total number of nodes (gates) in the dataset. It will create a directory under ../Path_PNA/data/ which includes:

• The extracted features will be dumped in feat.txt. The ith line in feat.txt represents the feature vector of the node ID = the ith line in count.txt
• The existence of an edge i between two vertices u and v is represented by the entry of ith line in link.txt
• The cell.txt file includes the mapping between node IDs and gate instances

Running the Conversion

1. Modify line 6 in ./perl_codes/netlist_to_subgraph_directed.pl and place the full path to theCircuit.pm.
2. Perform the conversion:

   $ cd ./perl_codes
   $ perl netlist_to_subgraph_directed.pl -i test_adder -f test_adder -m 0 > log_adder.txt
   $ cd ../

Degradation Estimation (Subgraph Regression)

STA Results and Labels

• We build degradation-aware libraries and perform STA. Here, we release the degradation information obtained from STA.
• The ./Path_PNA/data/degradation_info direcotry contains the extracted 1000 timing-paths per design and the corresponding delay degradation.
• For example ./Path_PNA/data/degradation_info/adder contains the following 4 files:
  • paths.txt the extracted timing paths and corresponding node IDs.
  • adder_degradation_std.txt the STD of the process-variation-induced degradation per path.
  • adder_degradation_max.txt the max process-variation-induced degradation per path.
  • adder_degradation_avg.txt the average process-variation-induced degradation per path.

Example: Predict the Average Process-Variation-Induced Delay Degradation per Path

To predict the average process-variation-induced delay degradation per adder path:

1. Copy the ./Path_PNA/data/degradation_info/adder/paths.txt to the generated ./Path_PNA/data/test_adder:

   $ cp ./Path_PNA/data/degradation_info/adder/paths.txt ./Path_PNA/data/test_adder/paths.txt

2. Copy the ./Path_PNA/data/degradation_info/adder/adder_degradation_avg.txt to the generated ./Path_PNA/data/test_adder as the label file:

   $ cp ./Path_PNA/data/degradation_info/adder/adder_degradation_avg.txt ./Path_PNA/data/test_adder/label.txt

3. Train the model and predict. The ./Path_PNA/Main.py first extracts subgraphs around the timing-paths and then trains a PNA model.

   $ cd ./Path_PNA
   $ python Main.py  --no-parallel  --file-name test_adder --links-name link.txt  --hop 1  --filename Results_adder_average.txt  >  log_adder_average.txt

4. The results will be in Results_adder_average.txt. Check the testing performance for the best validation loss.

Citation and Acknowledgement

If you find the code useful, please cite our paper:

• TCAD 2022:

@ARTICLE{gnn4rel,
author={Alrahis, Lilas and Knechtel, Johann and Klemme, Florian and Amrouch, Hussam and Sinanoglu, Ozgur},
journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
title={{GNN4REL}: Graph Neural Networks for Predicting Circuit Reliability Degradation},
year={2022},  volume={41},  number={11},  pages={3826-3837},
doi={10.1109/TCAD.2022.3197521}}

We owe many thanks to the authors of "Principal Neighbourhood Aggregation for Graph Nets", NeurIPS 2020, for making their PNA code available.