Cognitive SSD: A Deep Learning Engine for In-Storage Data Retrieval

Authors: 

Shengwen Liang and Ying Wang, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing; University of Chinese Academy of Sciences; Youyou Lu and Zhe Yang, Tsinghua University; Huawei Li and Xiaowei Li, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing; University of Chinese Academy of Sciences

Abstract: 

Data analysis and retrieval is a widely-used component in existing artificial intelligence systems. However, each request has to go through each layer across the I/O stack, which moves tremendous irrelevant data between secondary storage, DRAM, and the on-chip cache. This leads to high response latency and rising energy consumption. To address this issue, we propose Cognitive SSD, an energy-efficient engine for deep learning based unstructured data retrieval. In Cognitive SSD, a flash-accessing accelerator named DLG-x is placed by the side of flash memory to achieve near-data deep learning and graph search. Such functions of in-SSD deep learning and graph search are exposed to the users as library APIs via NVMe command extension. Experimental results on the FPGA-based prototype reveal that the proposed Cognitive SSD reduces latency by 69.9% on average in comparison with CPU based solutions on conventional SSDs, and it reduces the overall system power consumption by up to 34.4% and 63.0% respectively when compared to CPU and GPU based solutions that deliver comparable performance.

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BibTeX
@inproceedings {234940,
author = {Shengwen Liang and Ying Wang and Youyou Lu and Zhe Yang and Huawei Li and Xiaowei Li},
title = {Cognitive {SSD}: A Deep Learning Engine for In-Storage Data Retrieval},
booktitle = {2019 {USENIX} Annual Technical Conference ({USENIX} {ATC} 19)},
year = {2019},
isbn = {978-1-939133-03-8},
address = {Renton, WA},
pages = {395--410},
url = {https://www.usenix.org/conference/atc19/presentation/liang},
publisher = {{USENIX} Association},
}