Paper Info
Title
DeepStore: In-Storage Acceleration for Intelligent Queries
Abstract
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×.
Year
DOI
Venue
2019
10.1145/3352460.3358320
Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
Keywords
Field
DocType
Hardware Accelerators, In-Storage Computing, Information Retrieval, Intelligent Query, Solid-State Drive
Computer architecture,Locality of reference,Computer science,Cache,Search engine indexing,Real-time computing,Artificial intelligence,Solid-state drive,Deep learning,Design space exploration,Data access,Runtime system
Conference
ISBN
Citations 
PageRank 
978-1-4503-6938-1
2
0.36
References 
Authors
0
10
Name
Order
Citations
PageRank
Vikram Sharma Mailthody181.46
Zaid Qureshi251.06
Weixin Liang321.04
Ziyan Feng420.36
Simon Garcia de Gonzalo520.36
Youjie Li6130.96
Hubertus Franke71257104.86
Xiong Jinjun880186.79
Jian Huang920.36
Wen-mei W. Hwu104322511.62