{"id":122890,"date":"2022-01-28T16:33:10","date_gmt":"2022-01-28T07:33:10","guid":{"rendered":"http:\/\/175.125.95.178\/press\/22890\/"},"modified":"2026-04-18T01:25:34","modified_gmt":"2026-04-17T16:25:34","slug":"22890","status":"publish","type":"press-post","link":"http:\/\/ee.presscat.kr\/en\/presses\/22890\/","title":{"rendered":"Prof. Myoungsoo Jung\u2019s research team develops the world's first computational storage\/SSD accelerator capable of graph machine learning, surpassing NVIDIA GPU performance by 7 times"},"content":{"rendered":"
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EE Prof. Myoungsoo Jung\u2019s research team develops the world’s first computational storage\/SSD accelerator capable of graph machine learning, surpassing NVIDIA GPU performance by 7 times<\/p>\n

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Our department\u2019s Professor Myounsoo Jung\u2019s research team has developed the world\u2019s first computational SSD (CSSD) based accelerator to speed up the graph neural network (GNN)<\/p>\n

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The research team has developed the ‘holistic graph neural network technology (HolisticGNN)’, which directly accelerates graph neural network (GNN) near storage\/SSD device where the actual graph data exist. HolisticGNN outperforms GNN inference services compared to high-performance NVIDIA GPUs by 7x while reducing energy consumption by 33x.<\/p>\n

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Among the proposed research results, especially noteworthy is that HolisticGNN provides a software framework that allows users to easily program various GNN models and hardware logic\/RTLs for neural network acceleration that users can freely customize. The research team implemented the proposed HolisticGNN on their FPGA-based computational SSD prototype and verified the effectiveness of HolisticGNN.<\/p>\n

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HolisticGNN not only services high-speed GNN inference near storage\/SSD for large-scale graphs, but also processes GNN preprocessing such as graph transformation and graph sampling near non-volatile memory by<\/p>\n

securing a computational SSD acceleration system optimized for energy saving. This is expected to replace existing high-performance acceleration systems for a wide range of practical applications such as super-large recommendation systems, traffic prediction systems, and drug development.<\/p>\n

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The KAIST Ph.D. Candidates (Miryeong Kwon, Donghyun Gouk, and Sangwon Lee) participate in this research, and the paper (Hardware\/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs) will be reported in February at \u2018USENIX Conference on File and Storage Technologies, (FAST) 2022\u2019.<\/p>\n

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The research was supported by the Samsung Science & Technology Foundation. More information on this paper can be found at http:\/\/camelab.org<\/a><\/u><\/a>, and this result has been reported by domestic media as follow.<\/p>\n

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[Link]<\/p>\n

KAIST<\/p>\n

https:\/\/news.kaist.ac.kr\/news\/html\/news\/?mode=V&mng_no=18410<\/a><\/p>\n

Naver website <\/p>\n

https:\/\/search.naver.com\/search.naver?where=news&sm=tab_tnw&query=%EA%B7%B8%EB%9E%98%ED%94%84&sort=0&photo=0&field=0&pd=0&ds=&de=&mynews=0&office_type=0&office_section_code=0&news_office_checked=&related=1&docid=4210005835208&nso=so:r,p:all,a:all<\/a><\/u><\/a> <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

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