Tree-based machine learning performed in-memory with memristive analog CAM
Tree-based machine learning algorithms are known to be explainable and effective even trained on limited datasets, however difficult to optimize on conventional digital hardware. The authors apply analog content addressable memory to accelerate tree-based model inference for improved performance.
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| Auteurs principaux: | , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/2918fbbcb63643d0ba6ac994f9f06593 |
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| Résumé: | Tree-based machine learning algorithms are known to be explainable and effective even trained on limited datasets, however difficult to optimize on conventional digital hardware. The authors apply analog content addressable memory to accelerate tree-based model inference for improved performance. |
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