A Machine Learning Predictor and Corrector Framework to Identify and Resolve VLSI Routing Short Violations
| dc.contributor.advisor | Behjat, Laleh | |
| dc.contributor.advisor | Rakai, Logan M. | |
| dc.contributor.author | Fakheri Tabrizi, Aysa | |
| dc.contributor.committeemember | Yanushkevich, Svetlana N. | |
| dc.contributor.committeemember | Dimitrov, Vassil | |
| dc.date | 2019-06 | |
| dc.date.accessioned | 2018-10-25T16:27:50Z | |
| dc.date.available | 2018-10-25T16:27:50Z | |
| dc.date.issued | 2018-10-24 | |
| dc.description.abstract | The growth of Very Large Scale Integration (VLSI) technology provokes new challenges in design automation of Integrated Circuits (ICs). Routability is one of the most challenging aspects in Electronic Design Automation (EDA) that is faced in two consecutive phases of physical design: placement and routing. During placement, the exact locations of circuit components are determined. During routing the paths for all of the wires are specified. Routing is performed in two stages: global routing and detailed routing. Many of the violations that occur during the detailed routing stage stem from ignoring the routing rules during placement. Therefore, detecting and preventing routing violations in the placement stage has become critical in reducing the design time and the possibility of failure. In this thesis, Eh?Predictor, a deep learning framework to predict detailed routing short violations during placement is proposed. In the development of this predictor, relevant features, contributing to routing violations, were identified, extracted, and analyzed. A neural network model that can handle imbalanced data was customized to detect these violations using the defined features. The proposed predictor can be integrated into a placement tool and be used as a guide during the placement process to reduce the number of shorts happening in the detailed routing stage. One of the advantages of this technique is that by using the proposed deep learning-based predictor, global routing is no longer required as frequently. Hence the total runtime for place and route can be significantly reduced. In addition to Eh?Predictor, a detailed routing-aware detailed placement algorithm is developed to improve detailed routability in a relatively short runtime. The proposed technique is referred to as Detailed Routing-aware Detailed Placer (DrDp). DrDp is a heuristic that aims to reduce the local congestion and mitigate routing failure by aligning the connected cells where possible at the final stage of detailed placement process. Experimental results show that Eh?Predictor is able to predict on average 90% of the short violations of previously unseen data with only 5% false alarm rate and considerably reduce computational time, and DrDp can effectively improve the detailed routing quality in a short runtime with no significant change in detailed placement score or total wirelength. | en_US |
| dc.identifier.citation | Fakheri Tabrizi, A. (2018). A Machine Learning Predictor and Corrector Framework to Identify and Resolve VLSI Routing Short Violations (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/33224 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/33224 | |
| dc.identifier.uri | http://hdl.handle.net/1880/108915 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.faculty | Schulich School of Engineering | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Physical design | |
| dc.subject | Electronic design automation | |
| dc.subject | Computer aided design | |
| dc.subject | Placement | |
| dc.subject | Routing | |
| dc.subject | VLSI | |
| dc.subject | Machine Learning | |
| dc.subject.classification | Engineering--Electronics and Electrical | en_US |
| dc.title | A Machine Learning Predictor and Corrector Framework to Identify and Resolve VLSI Routing Short Violations | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| ucalgary.item.requestcopy | true |