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

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題2023 60th ACM/IEEE Design Automation Conference, DAC 2023
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9798350323481
DOIs
出版狀態Published - 2023
事件60th ACM/IEEE Design Automation Conference, DAC 2023 - San Francisco, United States
持續時間: 9 7月 202313 7月 2023

出版系列

名字Proceedings - Design Automation Conference
2023-July
ISSN(列印)0738-100X

Conference

Conference60th ACM/IEEE Design Automation Conference, DAC 2023
國家/地區United States
城市San Francisco
期間9/07/2313/07/23

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