Robust GNN-Based Representation Learning for HLS
The efficient and timely optimization of microarchitecture for a target application is hindered by the long evaluation runtime of a design candidate, creating a serious burden. To tackle this problem, researchers have started using learning algorithms such as graph neural networks (GNNs) to accelera...
Saved in:
| Published in: | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 1 - 9 |
|---|---|
| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
28.10.2023
|
| Subjects: | |
| ISSN: | 1558-2434 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The efficient and timely optimization of microarchitecture for a target application is hindered by the long evaluation runtime of a design candidate, creating a serious burden. To tackle this problem, researchers have started using learning algorithms such as graph neural networks (GNNs) to accelerate the process by developing a surrogate of the target tool. However, challenges arise when developing such models for HLS tools due to the program's long dependency range and deeply coupled input program and transformations (i.e., pragmas). To address them, in this paper, we present HARP ( H ierarchical A ugmentation for R epresentation with P ragma optimization) with a novel hierarchical graph representation of the HLS design by introducing auxiliary nodes to include high-level hierarchical information about the design. Additionally, HARP decouples the representation of the program and its transformations and includes a neural pragma transformer (NPT) approach to facilitate a more systematic treatment of this process. Our proposed graph representation and model architecture of HARP not only enhance the performance of the model and design space exploration based on it but also improve the model's transfer learning capability, enabling easier adaptation to new environments 1 1 All materials available at https://github.com/UCLA-VAST/HARP. |
|---|---|
| AbstractList | The efficient and timely optimization of microarchitecture for a target application is hindered by the long evaluation runtime of a design candidate, creating a serious burden. To tackle this problem, researchers have started using learning algorithms such as graph neural networks (GNNs) to accelerate the process by developing a surrogate of the target tool. However, challenges arise when developing such models for HLS tools due to the program's long dependency range and deeply coupled input program and transformations (i.e., pragmas). To address them, in this paper, we present HARP ( H ierarchical A ugmentation for R epresentation with P ragma optimization) with a novel hierarchical graph representation of the HLS design by introducing auxiliary nodes to include high-level hierarchical information about the design. Additionally, HARP decouples the representation of the program and its transformations and includes a neural pragma transformer (NPT) approach to facilitate a more systematic treatment of this process. Our proposed graph representation and model architecture of HARP not only enhance the performance of the model and design space exploration based on it but also improve the model's transfer learning capability, enabling easier adaptation to new environments 1 1 All materials available at https://github.com/UCLA-VAST/HARP. |
| Author | Cong, Jason Sohrabizadeh, Atefeh Bai, Yunsheng Sun, Yizhou |
| Author_xml | – sequence: 1 givenname: Atefeh surname: Sohrabizadeh fullname: Sohrabizadeh, Atefeh email: atefehsz@cs.ucla.edu organization: University of California - Los Angeles,Computer Science Department,USA – sequence: 2 givenname: Yunsheng surname: Bai fullname: Bai, Yunsheng email: yba@cs.ucla.edu organization: University of California - Los Angeles,Computer Science Department,USA – sequence: 3 givenname: Yizhou surname: Sun fullname: Sun, Yizhou email: yzsun@cs.ucla.edu organization: University of California - Los Angeles,Computer Science Department,USA – sequence: 4 givenname: Jason surname: Cong fullname: Cong, Jason email: cong@cs.ucla.edu organization: University of California - Los Angeles,Computer Science Department,USA |
| BookMark | eNo1j8FKAzEURaMo2Nb-gYv8wIwveckkWdZR28JQoa3rkkxfZEQzZTIu_HsL6upyFufAnbKr1CdijAsohQB3v67rxaM26KCUILEUgBKtxgs2d8ZZ1GeWUutLNhFa20IqVDdsmvM7wFmw1YTBtg9feeTLzaZ48JmOfEungTKl0Y9dn3hDfkhdeuOxH_iq2d2y6-g_Ms3_dsZen5_29apoXpbretEUnQQ1FgKCB2UVKOdVkMIGipVGgzG04FtoHQCZNgY0loyIAZwmW0VSUh_JCpyxu99uR0SH09B9-uH78P8QfwCAx0YH |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/ICCAD57390.2023.10323853 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9798350322255 |
| EISSN | 1558-2434 |
| EndPage | 9 |
| ExternalDocumentID | 10323853 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: NSF grantid: 2211557,1937599,2119643,2303037 funderid: 10.13039/100000001 – fundername: Cisco funderid: 10.13039/100004351 – fundername: NASA funderid: 10.13039/100000104 – fundername: Okawa Foundation funderid: 10.13039/501100004399 – fundername: Amazon Research funderid: 10.13039/501100005288 – fundername: SRC JUMP 2.0 Center funderid: 10.13039/100000028 |
| GroupedDBID | 6IE 6IF 6IH 6IL 6IN AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO FEDTE IEGSK IJVOP M43 OCL RIE RIL RIO |
| ID | FETCH-LOGICAL-i204t-10ba0484049a4b218bef65373fbc0ac0c900e7cfb378e71fb095e86fe425de813 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 23 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001116715100141&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:22:16 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i204t-10ba0484049a4b218bef65373fbc0ac0c900e7cfb378e71fb095e86fe425de813 |
| PageCount | 9 |
| ParticipantIDs | ieee_primary_10323853 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-Oct.-28 |
| PublicationDateYYYYMMDD | 2023-10-28 |
| PublicationDate_xml | – month: 10 year: 2023 text: 2023-Oct.-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationTitle | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design |
| PublicationTitleAbbrev | ICCAD |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0020286 |
| Score | 2.4344213 |
| Snippet | The efficient and timely optimization of microarchitecture for a target application is hindered by the long evaluation runtime of a design candidate, creating... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Adaptation models Computational modeling Microarchitecture Representation learning Runtime Systematics Transfer learning |
| Title | Robust GNN-Based Representation Learning for HLS |
| URI | https://ieeexplore.ieee.org/document/10323853 |
| WOSCitedRecordID | wos001116715100141&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwMhECa28aAXXzW-w8ErLVvYhb3aWGtiNk3VpLcG2MH0sjXt1t_vQLc-Dh68ERIgAxm-GZhvhpDbhOvSZZCyxIBnMk1KpnPuWepTEE7kwisfi02ootDTaT5uyOqRCwMAMfgMuqEZ__LLhVuHp7JeSP4mEF9apKVUtiFrfXlXCJTZNlSH573HAYqSKnTpu6FCeHc79lcVlQgiw4N_Ln9IOt90PDr-ApojsgPVMdn_kUnwhPDJwq5XNX0oCnaHwFTSSQxxbZhFFW3yqL5RNFLp6Om5Q16H9y-DEWuKIbB5n8sar0trUNskWvRGWgRmCz5LhRLeOm4cdznnoJy3QmlQibdoO4HOPKBSlqATcUra1aKCM0KlyawUBqyEUioAY3CaEh0b7bzQPD8nnSD87H2T72K2lfvij_5Lshe2ONzofX1F2vVyDddk133U89XyJp7SJ3WvkdI |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwED1BQQIWvor4JgNrWqd2YmelorSiRFUpUrfKds5VlxS1Kb8f26QFBga2yFKinK3zu7Pv3QO4j4jIdYJxGEk0IYujPBQpMWFsYqSaptRw48UmeJaJ8TgdVGR1z4VBRF98hg336O_y87leuaOypmv-Ri2-bMOOk86q6Fqb_MpCZbIu1iFps9e2xsTcJvUNpxHeWL_9S0fFw0jn8J8_cAT1b0JeMNhAzTFsYXECBz96CZ4CGc7ValkGT1kWPlhoyoOhL3KtuEVFUHVSnQY2TA26_dc6vHUeR-1uWMkhhLMWYaXdMJW0_sZsTC-ZstCs0CQx5dQoTaQmOiUEuTaKcoE8MspGTygSg9YtcxQRPYNaMS_wHAImE8WoRMUwZxxRSvuZ3KY2QhsqSHoBdWf85P2r48VkbfflH-N3sNcdvfQn_V72fAX7brrd_t4S11ArFyu8gV39Uc6Wi1u_Yp9DkpUb |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Digest+of+technical+papers+-+IEEE%2FACM+International+Conference+on+Computer-Aided+Design&rft.atitle=Robust+GNN-Based+Representation+Learning+for+HLS&rft.au=Sohrabizadeh%2C+Atefeh&rft.au=Bai%2C+Yunsheng&rft.au=Sun%2C+Yizhou&rft.au=Cong%2C+Jason&rft.date=2023-10-28&rft.pub=IEEE&rft.eissn=1558-2434&rft.spage=1&rft.epage=9&rft_id=info:doi/10.1109%2FICCAD57390.2023.10323853&rft.externalDocID=10323853 |