End-to-End Deep Learning of Optimization Heuristics
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 158740627
- Type
- E - Conference contribution
- DOI
-
10.1109/PACT.2017.24
- Title of conference / published proceedings
- Proceedings - 26th International Conference on Parallel Architectures and Compilation Techniques, PACT 2017
- First page
- 219
- Volume
- 2017-September
- Issue
- -
- ISSN
- 1089-795X
- Open access status
- Other exception
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - Computer Science
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "First to use deep learned programming language models for driving compiler optimisation decisions.
Best paper award in PACT 2017: 20% acceptance (46/226).
First author (PGR Cummins) won the SICSA award for Best CompSci PhD in Scotland - chapter 6 is this paper.
Invited talks at Google (Paris, Dec 2018), Huawei (Toronto, Nov 2018), and IBM (Toronto, Nov 2018).
On the basis of this work, the PGR was hired as Research Engineer by Facebook."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
