{"id":133052,"date":"2022-08-24T09:48:08","date_gmt":"2022-08-24T00:48:08","guid":{"rendered":"http:\/\/192.249.19.202\/?post_type=research-achieve&p=133052"},"modified":"2026-04-13T20:09:33","modified_gmt":"2026-04-13T11:09:33","slug":"ee-prof-rhu-minsoos-team-build-first-ever-privacy-aware-a-i-semiconductor-speeding-up-the-differentially-private-learning-process","status":"publish","type":"research-achieve","link":"http:\/\/ee.presscat.kr\/en\/research-achieve\/ee-prof-rhu-minsoos-team-build-first-ever-privacy-aware-a-i-semiconductor-speeding-up-the-differentially-private-learning-process\/","title":{"rendered":"EE Prof. Rhu, Minsoo\u2019s Team Build First-ever Privacy-aware A. I Semiconductor, Speeding Up the Differentially Private Learning Process"},"content":{"rendered":"
\n

EE Professor Rhu, Minsoo\u2019s Research Team Build First-ever Privacy-aware Artificial Intelligence Semiconductor, Speeding Up the Differentially Private Learning Process 3.6 Times Google\u2019s TPUv3<\/strong><\/span><\/p>\n

 <\/p>\n<\/blockquote>\n

\"\"<\/span><\/p>\n

[Professor Rhu, Minsoo]<\/span><\/p>\n

\u00a0<\/p>\n

EE Professor Rhu and his research team have taken artificial intelligence semiconductors a big leap forward in the application of differentially private machine learning. Professor Rhu\u2019s team analyzed the bottleneck component in the differentially private machine learning performance and devised a semiconductor chip greatly improving differentially private machine learning application performance.<\/span><\/p>\n

Professor Rhu\u2019s artificial intelligence chip consists of, among others, a cross-product-based arithmetic unit and an addition tree-based post-processing arithmetic unit and is capable of 3.6 times faster machine learning process compared with that of Google\u2019s TPUv3, today\u2019s most widely used AI processor. <\/span><\/p>\n

The new chip also boasts comparable performance to that of NVIDIA\u2019s A100 GPU, even with 10 times less resources.<\/span><\/p>\n

\u00a0<\/p>\n

\"\"<\/span><\/p>\n

[From left, Co-lead authors Park, Beomsik and Hwang, Ranggi; co-authors Yoon, Dongho and Choi, Yoonhyuk]<\/span><\/p>\n

\u00a0<\/p>\n

This work, with EE researchers Park, Beomsik and Hwang, Ranggi as co-first authors, will be presented as\u00a0DiVa: An Accelerator for Differentially Private Machine Learning\u00a0at the 55th IEEE\/ACM International Symposium on Microarchitectures (MICRO 2022), the premier research venue for computer architecture research coming October 1 through 5 in Chicago, USA.<\/span><\/p>\n

Professor Rhu\u2019s achievements have been reported in multiple press coverage.<\/span><\/p>\n

\u00a0<\/p>\n

Links:<\/span><\/p>\n

AI Times:\u00a0http:\/\/www.aitimes.com\/news\/articleView.html?idxno=146435<\/a><\/span><\/p>\n

Yonhap :\u00a0https:\/\/www.yna.co.kr\/view\/AKR20201116072400063?input=1195m<\/a><\/span><\/p>\n

Financial News\u00a0:\u00a0https:\/\/www.fnnews.com\/news\/202208212349474072<\/a><\/span><\/p>\n

Donga Science :\u00a0https:\/\/www.dongascience.com\/news.php?idx=55893<\/a><\/span><\/p>\n

Industry News :\u00a0http:\/\/www.industrynews.co.kr\/news\/articleView.html?idxno=46829<\/a><\/span><\/p>\n

Boan :\u00a0https:\/\/www.boannews.com\/media\/view.asp?idx=108883&kind=<\/a><\/span><\/div>\n
\u00a0<\/div>\n","protected":false},"excerpt":{"rendered":"

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