Tree-based machine learning performed in-memory with memristive analog CAM

Giacomo Pedretti (), Catherine E. Graves (), Sergey Serebryakov, Ruibin Mao, Xia Sheng, Martin Foltin, Can Li and John Paul Strachan ()
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Giacomo Pedretti: Hewlett Packard Labs, Hewlett Packard Enterprise
Catherine E. Graves: Hewlett Packard Labs, Hewlett Packard Enterprise
Sergey Serebryakov: Hewlett Packard Labs, Hewlett Packard Enterprise
Ruibin Mao: The University of Hong Kong
Xia Sheng: Hewlett Packard Labs, Hewlett Packard Enterprise
Martin Foltin: Hewlett Packard Labs, Hewlett Packard Enterprise
Can Li: Hewlett Packard Labs, Hewlett Packard Enterprise
John Paul Strachan: Forschungszentrum Jülich GmbH

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Tree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer improved interpretability compared to Deep Neural Networks (DNN). However, these models are difficult to optimize for fast inference at scale without accuracy loss in von Neumann architectures due to non-uniform memory access patterns. Recently, we proposed a novel analog content addressable memory (CAM) based on emerging memristor devices for fast look-up table operations. Here, we propose for the first time to use the analog CAM as an in-memory computational primitive to accelerate tree-based model inference. We demonstrate an efficient mapping algorithm leveraging the new analog CAM capabilities such that each root to leaf path of a Decision Tree is programmed into a row. This new in-memory compute concept for enables few-cycle model inference, dramatically increasing 103 × the throughput over conventional approaches.

Date: 2021
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DOI: 10.1038/s41467-021-25873-0

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