Optical content-addressable memories for tree-based machine learning

In this work, we describe the hardware implementation of a decision tree (DT) model using our microring-based timedivision multiplexing (TDM) optical content-addressable memory (O-CAM) architecture, which uses a customized encoding scheme that allows ternary search. Mainstream machine learning models, such as neural networks and transformers, are capable of impressive accuracy when dealing with unstructured data such as images or text, but DTs still outperform them for regression and classification tasks over tabular data. We developed a novel DT mapping scheme for O-CAM and a new inequality encoding technique, enabling the parallel evaluation of inequalities for all nodes of the DT.

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Publication Details

Published
2025-01-23
DOI
https://doi.org/10.1117/12.3037316
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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Optical content-addressable memories for tree-based machine learning

Yanir London, Stanley Cheung, Thomas Van Vaerenbergh, Yiwei Peng et al.
Neural Networks and Reservoir Computing
article

Optical content-addressable memories for tree-based machine learning

Yanir London, Stanley Cheung, Thomas Van Vaerenbergh, Yiwei Peng, Marco Fiorentino, Matéj Hejda, Raymond G. Beausoleil, Luca Buonanno, Lei Zhao, Giacomo Pedretti, Bassem Tossoun, Yuan Yuan
article en

Abstract

In this work, we describe the hardware implementation of a decision tree (DT) model using our microring-based timedivision multiplexing (TDM) optical content-addressable memory (O-CAM) architecture, which uses a customized encoding scheme that allows ternary search. Mainstream machine learning models, such as neural networks and transformers, are capable of impressive accuracy when dealing with unstructured data such as images or text, but DTs still outperform them for regression and classification tasks over tabular data. We developed a novel DT mapping scheme for O-CAM and a new inequality encoding technique, enabling the parallel evaluation of inequalities for all nodes of the DT.

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Neural Networks and Reservoir Computing
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Optical content-addressable memories for tree-based machine learning — Yanir London, Stanley Cheung, et al. · (2025) | TGRS Research Map | TGRS