Deep Reinforcement Learning for Resource Constrained HLS Scheduling
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...
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| Format: | masterThesis |
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2022
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| Online Access: | http://hdl.handle.net/10725/13937 https://doi.org/10.26756/th.2022.419 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
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| _version_ | 1864513468686663680 |
|---|---|
| author | Makhoul, Rim |
| author_facet | Makhoul, Rim |
| author_role | author |
| dc.creator.none.fl_str_mv | Makhoul, Rim |
| dc.date.none.fl_str_mv | 2022-08-16T09:00:27Z 2022-08-16T09:00:27Z 2022 2022-05-23 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/13937 https://doi.org/10.26756/th.2022.419 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Lebanese American University |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Integrated circuits -- Very large scale integration -- Computer simulation Reinforcement learning Lebanese American University -- Dissertations Dissertations, Academic |
| dc.title.none.fl_str_mv | Deep Reinforcement Learning for Resource Constrained HLS Scheduling |
| dc.type.none.fl_str_mv | Thesis info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/masterThesis |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| id | LAURepo_40337c6dec9bd4080f2b19cb29b082ce |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/13937 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Lebanese American University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Deep Reinforcement Learning for Resource Constrained HLS SchedulingMakhoul, RimIntegrated circuits -- Very large scale integration -- Computer simulationReinforcement learningLebanese American University -- DissertationsDissertations, AcademicHigh-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.1 online resource (xi, 55 leaves): ill. (some col.)Bibliography: leaf 53-55.Lebanese American University2022-08-16T09:00:27Z2022-08-16T09:00:27Z20222022-05-23Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/13937https://doi.org/10.26756/th.2022.419http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.phpeninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/139372022-08-23T06:17:34Z |
| spellingShingle | Deep Reinforcement Learning for Resource Constrained HLS Scheduling Makhoul, Rim Integrated circuits -- Very large scale integration -- Computer simulation Reinforcement learning Lebanese American University -- Dissertations Dissertations, Academic |
| status_str | publishedVersion |
| title | Deep Reinforcement Learning for Resource Constrained HLS Scheduling |
| title_full | Deep Reinforcement Learning for Resource Constrained HLS Scheduling |
| title_fullStr | Deep Reinforcement Learning for Resource Constrained HLS Scheduling |
| title_full_unstemmed | Deep Reinforcement Learning for Resource Constrained HLS Scheduling |
| title_short | Deep Reinforcement Learning for Resource Constrained HLS Scheduling |
| title_sort | Deep Reinforcement Learning for Resource Constrained HLS Scheduling |
| topic | Integrated circuits -- Very large scale integration -- Computer simulation Reinforcement learning Lebanese American University -- Dissertations Dissertations, Academic |
| url | http://hdl.handle.net/10725/13937 https://doi.org/10.26756/th.2022.419 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |