X-TIME: Accelerating Large Tree Ensembles Inference for Tabular Data With Analog CAMs

X-TIME: Accelerating Large Tree Ensembles Inference for Tabular Data With Analog CAMs

Structured, or tabular, data are the most common format in data science. While deep learning models have proven formidable in learning from unstructured data such as images or speech, they are less accurate than simpler approaches when learning from tabular data. In contrast, modern tree-based machi...

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Bibliographic Details
Main Authors: Giacomo Pedretti, John Moon, Pedro Bruel, Sergey Serebryakov, Ron M. Roth, Luca Buonanno, Archit Gajjar, Lei Zhao, Tobias Ziegler, Cong Xu, Martin Foltin, Paolo Faraboschi, Jim Ignowski, Catherine E. Graves
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Subjects:
Content addressable memories (CAMs)
decision trees
in-memory computing
RRAM
Online Access:https://ieeexplore.ieee.org/document/10753423/
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