Deployment of ML in Changing Environments Article Swipe
Marco Barbone
,
Christopher S. Brown
,
Benjamin Charles Radburn-Smith
,
A. Tapper
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202429509037
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202429509037
The High-Luminosity LHC upgrade of the CMS experiment will utilise a large number of Machine Learning (ML) based algorithms in its hardware-based trigger. These ML algorithms will facilitate the selection of potentially interesting events for storage and offline analysis. Strict latency and resource requirements limit the size and complexity of these models due to their use in a high-speed trigger setting and deployment on FPGA hardware. It is envisaged that these ML models will be trained on large, carefully tuned, Monte Carlo datasets and subsequently deployed in a real-world detector environment. Not only is there a potentially large difference between the MC training data and real-world conditions but these detector conditions could change over time leading to a shift in model output which could degrade trigger performance. The studies presented explore different techniques to reduce the impact of this effect, using the CMS track finding and vertex trigger algorithms as a test case. The studies compare a baseline retraining and redeployment of the model and episodic training of a model as new data arrives in a continual learning context. The results show that a continually learning algorithm outperforms a simple retrained model when degradation in detector performance is applied to the training data and is a viable option for maintaining performance in an evolving environment such as the High-Luminosity LHC.
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Metadata
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- article
- Language
- en
- Landing Page
- http://doi.org/10.1051/epjconf/202429509037
- OA Status
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All OpenAlex metadata
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https://doi.org/10.1051/epjconf/202429509037Digital Object Identifier
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Deployment of ML in Changing EnvironmentsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-01-01Full publication date if available
- Authors
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Marco Barbone, Christopher S. Brown, Benjamin Charles Radburn-Smith, A. TapperList of authors in order
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https://doi.org/10.1051/epjconf/202429509037Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1051/epjconf/202429509037Direct OA link when available
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Software deployment, Computer science, Systems engineering, Engineering, Software engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.continually | 184 |
| abstract_inverted_index.degradation | 193 |
| abstract_inverted_index.environment | 214 |
| abstract_inverted_index.interesting | 32 |
| abstract_inverted_index.maintaining | 209 |
| abstract_inverted_index.outperforms | 187 |
| abstract_inverted_index.performance | 196, 210 |
| abstract_inverted_index.potentially | 31, 96 |
| abstract_inverted_index.environment. | 90 |
| abstract_inverted_index.performance. | 126 |
| abstract_inverted_index.redeployment | 160 |
| abstract_inverted_index.requirements | 43 |
| abstract_inverted_index.subsequently | 84 |
| abstract_inverted_index.hardware-based | 21 |
| abstract_inverted_index.High-Luminosity | 1, 218 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.06037501 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |