A Reinforcement Learning-Based Framework to Generate Routing Solutions and Correct Violations in VLSI Physical Design
| dc.contributor.advisor | Behjat, Laleh | |
| dc.contributor.author | Gandhi, Upma | |
| dc.contributor.committeemember | Dimitrov, Vassil S. | |
| dc.contributor.committeemember | Yanushkevich, Svetlana N. | |
| dc.date | 2020-06 | |
| dc.date.accessioned | 2020-01-20T17:29:44Z | |
| dc.date.available | 2020-01-20T17:29:44Z | |
| dc.date.issued | 2020-01-15 | |
| dc.description.abstract | The impact of this modern era has given rise to the demand for compact electronic devices like mobile phones. With the decrease in the devices’ size, the pressure lands upon making more compact and efficient integrated circuits (IC). The process of making an IC is called Very Large Scale Integration (VLSI). Under this process, a physical design step takes place in which the physical shapes of circuit elements are determined. During physical design, all the standard cells on the circuit are placed. This process is called placement. Then these cells are connected by wires which is called routing. Routing is one of the most difficult and time-consuming parts of physical design, where over a million connections have to be routed in a 3D arrangement while following strict design and manufacturing rules. The contributions presented in this thesis aim to automate the routing process through machine learning (ML) methods and remove any rule violations. The first contribution is called Alpha-router, a multiplayer game model to perform the routing step using a type of ML method called reinforcement learning (RL). In RL, no external data is required in training the neural network. As a proof of concept, a small grid based circuit is used. The obtained routing results with Alpha-router show good performance with different difficulty levels of cell placement on the circuit. The parameters experimentally found are compared with [1], which is RL based game model with similar complexity and grid-based environment. The second contribution discussed in the thesis is called Alpha-PD-Router. The Alpha-PD-Router is a combined routing and correction technique that corrects the violations occurring in routed circuits. Testing with 99 cases, the final iteration of Alpha-PD-Router achieved to resolve 116 violations out of 177 violations. The research presented in this thesis is aimed to open a new gateway to routing tools which don’t require any human intervention and can cope up with the ever-advancing needs of new technologies. | en_US |
| dc.identifier.citation | Gandhi, U. (2020). A Reinforcement Learning-Based Framework to Generate Routing Solutions and Correct Violations in VLSI Physical Design (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/37475 | |
| dc.identifier.uri | http://hdl.handle.net/1880/111523 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of 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. | en_US |
| dc.subject | Reinforcement Learning | en_US |
| dc.subject | Physical Design | en_US |
| dc.subject | Routing | en_US |
| dc.subject.classification | Artificial Intelligence | en_US |
| dc.subject.classification | Computer Science | en_US |
| dc.subject.classification | Engineering--Electronics and Electrical | en_US |
| dc.title | A Reinforcement Learning-Based Framework to Generate Routing Solutions and Correct Violations in VLSI Physical Design | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Electrical & Computer | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |