https://scholars.lib.ntu.edu.tw/handle/123456789/636156
標題: | Keep in Balance: Runtime-reconfigurable Intermittent Deep Inference | 作者: | Yen, Chih Hsuan Mendis, Hashan Roshantha TEI-WEI KUO Hsiu, Pi Cheng |
關鍵字: | Deep neural networks | energy harvesting | intermittent systems | runtime reconfiguration | tinyML | 公開日期: | 9-九月-2023 | 卷: | 22 | 期: | 5 s | 來源出版物: | ACM Transactions on Embedded Computing Systems | 摘要: | Intermittent deep neural network (DNN) inference is a promising technique to enable intelligent applications on tiny devices powered by ambient energy sources. Nonetheless, intermittent execution presents inherent challenges, primarily involving accumulating progress across power cycles and having to refetch volatile data lost due to power loss in each power cycle. Existing approaches typically optimize the inference configuration to maximize data reuse. However, we observe that such a fixed configuration may be significantly inefficient due to the fluctuating balance point between data reuse and data refetch caused by the dynamic nature of ambient energy.This work proposes DynBal, an approach to dynamically reconfigure the inference engine at runtime. DynBal is realized as a middleware plugin that improves inference performance by exploring the interplay between data reuse and data refetch to maintain their balance with respect to the changing level of intermittency. An indirect metric is developed to easily evaluate an inference configuration considering the variability in intermittency, and a lightweight reconfiguration algorithm is employed to efficiently optimize the configuration at runtime. We evaluate the improvement brought by integrating DynBal into a recent intermittent inference approach that uses a fixed configuration. Evaluations were conducted on a Texas Instruments device with various network models and under varied intermittent power strengths. Our experimental results demonstrate that DynBal can speed up intermittent inference by 3.26 times, achieving a greater improvement for a large network under high intermittency and a large gap between memory and computation performance. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/636156 | ISSN: | 15399087 | DOI: | 10.1145/3607918 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。