Eco-feller: Minimizing the Energy Consumption of Random Forest Algorithm by an Eco-pruning Strategy over MLC NVRAM
學年 110
學期 1
發表日期 2021-12-05
作品名稱 Eco-feller: Minimizing the Energy Consumption of Random Forest Algorithm by an Eco-pruning Strategy over MLC NVRAM
作品名稱（其他語言）
著者 Yu-Pei Liang; Yung-Han Hsu; Tseng-Yi Chen; Shuo-Han Chen; Hsin-Wen Wei; Tsan-sheng Hsu; Wei-Kuan Shih
作品所屬單位
出版者
會議名稱 58th ACM/IEEE Design Automation Conference, DAC 2021
會議地點 State of California, USA
摘要 Random forest has been widely used to classifying objects recently because of its efficiency and accuracy. On the other hand, nonvolatile memory has been regarded as a promising candidate to be a part of a hybrid memory architecture. For achieving the higher accuracy, random forest tends to construct lots of decision trees, and then conducts some post-pruning methods to fell low contribution trees for increasing the model accuracy and space utilization. However, the cost of writing operations is always very high on non-volatile memory. Therefore, writing the to-be-pruned trees into non-volatile memory will significantly waste both energy and time. This work proposed a framework to ease such hurt of training a random forest model. The main spirit of this work is to evaluate the importance of trees before constructing it, and then adopts different writing modes to write the trees to the non-volatile memory space. The experimental results show the proposed framework can significantly mitigate the waste of energy with high accuracy.
關鍵字
語言 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20211205~20211209
通訊作者
國別 USA
公開徵稿
出版型式
出處
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