Artificial Neural Network Based Post-CTS QoR Report Prediction

Jain, Arpit and Das, Pabitra and Acharyya, Amit (2022) Artificial Neural Network Based Post-CTS QoR Report Prediction. In: 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022, 27 May-1 June 2022, Austin.

[img] Text
Proceedings_IEEE_International_12.pdf - Published Version
Restricted to Registered users only

Download (365kB) | Request a copy


In this paper, we propose two models to predict 26 parameters data of post clock tree synthesis (post-CTS) quality of results (QoR) report without running the CTS optimization step. In model 1, we considered 9 benchmark circuits (6 from ISCAS89 and 3 from open cores). We randomly split 50% of the total data into the training sample and the other 50% in the testing sample. In model 2, we use 6 benchmark circuit data for training purposes and use 3 benchmark circuit data for testing purposes which are unseen to the model. We utilize a regression neural network for predictions. To ensure robustness and reusability of the proposal, we validate our proposed models for two different technology nodes i.e. TSMC 65nm and TSMC 90nm. Experimental results show that the average mean square error for all the parameters for both the technologies is of the order of 10-3 while most of the parameter MSE is in the range of 10-5 to 10-7 for both the technology nodes. These data ensure robustness and re-usability of the proposal with a high level of accuracy. © 2022 IEEE.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Acharyya, Amit
Item Type: Conference or Workshop Item (Paper)
Additional Information: ACKNOWLEDGMENT Dr. Acharyya would like to acknowledge the support received from the Taiwan Semiconductor Manufacturing Company Limited.
Uncontrolled Keywords: machine learning in EDA; post-CTS; QoR report
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Ms Palak Jain
Date Deposited: 22 May 2023 09:29
Last Modified: 22 May 2023 09:29
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 11477 Statistics for this ePrint Item