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Towards Scalable Analytics with Inference-Enabled Solid-State Drives

Title
Towards Scalable Analytics with Inference-Enabled Solid-State Drives
Authors
Kim, MinsubKung, JaehaLee, Sungjin
DGIST Authors
Kim, Minsub; Kung, JaehaLee, Sungjin
Issue Date
2020-05
Citation
IEEE Computer Architecture Letters, 19(1), 13-17
Type
Article
Article Type
Article
Author Keywords
Image annotationAccelerationHardwareServersField programmable gate arraysTask analysisComputer architectureSolid-state drivesin-storage processingdeep neural networksconvolutional neural networks
ISSN
1556-6056
Abstract
In this paper, we propose a novel storage architecture, called an Inference-Enabled SSD (IESSD), which employs FPGA-based DNN inference accelerators inside an SSD. IESSD is capable of performing DNN operations inside an SSD, avoiding frequent data movements between application servers and data storage. This boosts up analytics performance of DNN applications. Moreover, by placing accelerators near data within an SSD, IESSD delivers scalable analytics performance which improves with the amount of data to analyze. To evaluate its effectiveness, we implement an FPGA-based proof-of-concept prototype of IESSD and carry out a case study with an image tagging (classification) application. Our preliminary results show that IESSD exhibits 1.81x better performance, achieving 5.31x lower power consumption, over a conventional system with GPU accelerators.
URI
http://hdl.handle.net/20.500.11750/10992
DOI
10.1109/LCA.2019.2930590
Publisher
Institute of Electrical and Electronics Engineers
Related Researcher
  • Author Lee, Sungjin Data-Intensive Computing Systems Laboratory
  • Research Interests Computer System, System Software, Storage System, Non-volatile Memory, Flash-based SSD, Distributed Storage Systems
Files:
There are no files associated with this item.
Collection:
Department of Information and Communication EngineeringIntelligent Digital Systems Lab1. Journal Articles
Department of Information and Communication EngineeringData-Intensive Computing Systems Laboratory1. Journal Articles


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