Optimizing Convolution Neural Nets with a Unified Transformation Approach.
The article explores how deep learning models have evolved from relying on hand-crafted operator libraries to utilizing compiler-based approaches for optimization, especially with the growing diversity of hardware platforms. It highlights the Apache TVM project, which allows machine learning enginee...
| Publicado en: | Communications of the ACM Vol. 67; no. 10; pp. 91 - 92 |
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| Formato: | Opinion |
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Association for Computing Machinery
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=179943076&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 179943076 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Oct2024 vid: 67 iid: 10 pid: 68 pub: Association for Computing Machinery artinfo: ui: 179943076 10.1145/3657630 ppf: 91 ppct: 1 formats: tig: atl: Optimizing Convolution Neural Nets with a Unified Transformation Approach. aug: au: Ceze, Luis affil: University of Washington, Paul G. Allen School of Computer Science and Engineering, Seattle, WA, USA su: Deep learning Machine learning Computer architecture Mathematical optimization Computer input-output equipment Compilers (Computer programs) sug: subj: Deep learning Machine learning Computer architecture Mathematical optimization Computer input-output equipment Compilers (Computer programs) ab: The article explores how deep learning models have evolved from relying on hand-crafted operator libraries to utilizing compiler-based approaches for optimization, especially with the growing diversity of hardware platforms. It highlights the Apache TVM project, which allows machine learning engineers to compile models for specific hardware targets, optimizing performance without altering model accuracy. The article points to an accompanying paper that proposes a unified transformation approach that optimizes model architectures through program transformations, avoiding expensive retraining and achieving significant performance gains without compromising accuracy. pubtype: Periodical doctype: Opinion src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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