Tsai, Chieh LinChieh LinTsaiWu, Chun FengChun FengWuChang, Yuan HaoYuan HaoChangHu, Han WenHan WenHuLee, Yung ChunYung ChunLeeLi, Hsiang PangHsiang PangLiTEI-WEI KUO2023-10-262023-10-262023-01-0197983503234810738100Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/636599Random 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.[SDGs]SDG7conference paper10.1109/DAC56929.2023.102476952-s2.0-85173126933https://api.elsevier.com/content/abstract/scopus_id/85173126933