Deep Reinforcement Learning for Resource Constrained HLS Scheduling

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dc.contributor.author Makhoul, Rim
dc.date.accessioned 2022-08-16T09:00:27Z
dc.date.available 2022-08-16T09:00:27Z
dc.date.copyright 2022 en_US
dc.date.issued 2022-05-23
dc.identifier.uri http://hdl.handle.net/10725/13937
dc.description.abstract High-level synthesis (HLS) scheduling, an NP-hard problem, is a process that auto-mates VLSI design and is a very important step in silicon compilation. HLS takes as input a behavioral description of a system with a set of constraints and outputs an RTL description of a digital system. The two main steps in HLS are: operations scheduling and data-path allocation. In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. The actions and rewards for the proposed algorithm are selected carefully to guide the agent to its objective. We used a deep neural network to train the agent and in order to learn the the Q-values. The results of this work are compared to other state-of-the-art algorithms and are proven to be very effective and promising. en_US
dc.language.iso en en_US
dc.subject Integrated circuits -- Very large scale integration -- Computer simulation en_US
dc.subject Reinforcement learning en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Deep Reinforcement Learning for Resource Constrained HLS Scheduling en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201202605 en_US
dc.author.commembers Mourad, Azzam
dc.author.commembers El Khatib, Nader
dc.author.department Computer Science And Mathematics en_US
dc.description.physdesc 1 online resource (xi, 55 leaves): ill. (some col.) en_US
dc.author.advisor Harmanani, Haidar
dc.keywords Very-large-scale integration en_US
dc.keywords VLSI en_US
dc.keywords High-level synthesis en_US
dc.keywords HLS en_US
dc.keywords Scheduling en_US
dc.keywords Resource constraints en_US
dc.keywords Reinforcement learning en_US
dc.keywords Deep neural networks en_US
dc.description.bibliographiccitations Bibliography: leaf 53-55. en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.419
dc.author.email rim.makhoul@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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