A digital 3D TCAM accelerator for the inference phase of Random Forest

Chieh Lin Tsai*, Chun Feng Wu, Yuan Hao Chang, Han Wen Hu, Yung Chun Lee, Hsiang Pang Li, Tei Wei Kuo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Random forest is a popular ensemble machine-learning algorithm for classification and regression tasks. However, the irregular tree shapes and non-deterministic memory access patterns make it hard for the current von Neumann architecture to handle random forest efficiently. This paper proposes a digital 3D TCAM-based accelerator for the random forest, adopting the idea of processing-in-memory (PIM) to reduce data movement. By utilizing this accelerator, we propose a TCAM-based approach to provide real-time inference with low energy consumption, making it suitable for edge or embedded environments. In the experiments, the proposed approach achieves an average of 3.13 times higher throughput with 22 times more energy saving than the GPU approach.

Original languageEnglish
Title of host publication2023 60th ACM/IEEE Design Automation Conference, DAC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350323481
DOIs
StatePublished - 2023
Event60th ACM/IEEE Design Automation Conference, DAC 2023 - San Francisco, United States
Duration: 9 Jul 202313 Jul 2023

Publication series

NameProceedings - Design Automation Conference
Volume2023-July
ISSN (Print)0738-100X

Conference

Conference60th ACM/IEEE Design Automation Conference, DAC 2023
Country/TerritoryUnited States
CitySan Francisco
Period9/07/2313/07/23

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