DeepStore: In-Storage Acceleration for Intelligent Queries
By Vikram Sharma Mailthody, Zaid Qureshi, Weixin Liang, Ziyan Feng, Simon Garcia De Gonzalo, Youjie Li, H. Franke, Jinjun Xiong, Jian Huang, Wen-mei W. Hwu. 2019
93 citations recorded by the source corpus.
Recent advancements in deep learning techniques facilitate intelligent-query support in diverse applications, such as content-based image retrieval and audio texturing. Unlike conventional key-based queries, these intelligent queries lack efficient indexing and require complex compute operations for feature matching. To achieve high-performance intelligent querying against massive datasets, modern computing systems employ GPUs in-conjunction with solid-state drives (SSDs) for fast data access and parallel data processing. However, our characterization with various intelligent-query workloads developed with deep neural networks (DNNs), shows that the storage I/O bandwidth is still the major bottleneck that contributes 56%--90% of the query execution time. To this end, we present DeepStore, an in-storage accelerator architecture for intelligent queries. It consists of (1) energy-efficient in-storage accelerators designed specifically for supporting DNN-based intelligent queries, under the resource constraints in modern SSD controllers; (2) a similarity-based in-storage query cache to exploit the temporal locality of user queries for further performance improvement; and (3) a lightweight in-storage runtime system working as the query engine, which provides a simple software abstraction to support different types of intelligent queries. DeepStore exploits SSD parallelisms with design space exploration for achieving the maximal energy efficiency for in-storage accelerators. We validate DeepStore design with an SSD simulator, and evaluate it with a variety of vision, text, and audio based intelligent queries. Compared with the state-of-the-art GPU+SSD approach, DeepStore improves the query performance by up to 17.7×, and energy-efficiency by up to 78.6×.
Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, ML Systems & Hardware
How researchers see this paper
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In Artificial Intelligence
- Weixin Liang
- Zhou Yu
- James Y. Zou
- Kai-Wei Chang
- Hinrich Schütze
- Timo Schick
- Adina Williams
- Mohit Bansal
In Computer Vision and Pattern Recognition
In ML Systems & Hardware
Citations in the corpus
- ALICE: Active Learning with Contrastive Natural Language Explanations (2020, neutral): “potential for reducing computation runtime (Chen et al., 2020; Mailthody et al., 2019).”
- Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (2020, background): “There are many other examples where humans find it easier to perform pairwise comparisons rather than providing direct labels (Simpson and Gurevych, 2018; Mailthody et al., 2019; Liang et al., 2018), including content search (Fürnkranz and Hüllermeier, 2010), image retrieval (Wah et al.”