每年專案
摘要
Many prior research works have been widely discussed how to bring machine learning algorithms to embedded systems. Because of resource constraints, embedded platforms for machine learning applications play the role of a predictor. That is, an inference model will be constructed on a personal computer or a server platform, and then integrated into embedded systems for just-in-time inference. With the consideration of the limited main memory space in embedded systems, an important problem for embedded machine learning systems is how to efficiently move inference model between the main memory and a secondary storage (e.g., flash memory). For tackling this problem, we need to consider how to preserve the locality inside the inference model during model construction. Therefore, we have proposed a solution, namely locality-aware random forest (LaRF), to preserve the inter-locality of all decision trees within a random forest model during the model construction process. Owing to the locality preservation, LaRF can improve the read latency by 81.5% at least, compared to the original random forest library.
原文 | ???core.languages.en_GB??? |
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主出版物標題 | Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022 |
發行者 | Institute of Electrical and Electronics Engineers Inc. |
ISBN(電子) | 9781450392174 |
DOIs | |
出版狀態 | 已出版 - 30 10月 2022 |
事件 | 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022 - San Diego, United States 持續時間: 30 10月 2022 → 4 11月 2022 |
出版系列
名字 | IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD |
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ISSN(列印) | 1092-3152 |
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???event.eventtypes.event.conference??? | 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022 |
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國家/地區 | United States |
城市 | San Diego |
期間 | 30/10/22 → 4/11/22 |
指紋
深入研究「On minimizing the read latency of flash memory to preserve inter-tree locality in random forest」主題。共同形成了獨特的指紋。專案
- 2 已完成
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用於高效能機器學習框架之新興記憶體與儲存系統設計-於混合式記憶儲存平台上高效能資料索引系統設計以支援機器學習應用(1/3)
Chen, T.-Y. (PI)
1/08/22 → 31/07/23
研究計畫: Research
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