Professor Min-Soo Rhu(Department of Electrical Engineering, adjunct Prof. in Graduate School of AI)’s lab has succeeded in
developing an “AI-based recommendation technology” acceleration system and their work has been reported in the media.
The system accelerates AI-based recommendation algorithms from 6x up to 17x.
The AI-based recommendation service refers to advertisement recommendations that can be found easily on portal sites
such as Google and Naver. This is a service based on a deep learning system and recommends personalized information
by using a user’s search history through AI technology. The key element of such service is the “computation time of the algorithm”.
Since it is based on real-time information, the quality of the service is determined by how quickly the recommendation results
are derived. Besides, this is directly related to user satisfaction and also the profit generation of the company.
Professor Min-Soo Rhu’s research team has devised a solution to effectively reduce execution time by developing an AI accelerator
computing system based on memory, which improves the so-called ‘memory bottleneck’.
The system proposed by the team is a ‘Processing in Memory (PI614M)’ technology that places an AI accelerator close to the memory
This is noteworthy in that it effectively reduces data transfers and memory access times.
The developed technology is receiving positive reviews and can be utilized in various fields.
Professor Min-Soo Rhu has expressed his position to cooperate with domestic companies to win the leadership of the AI accelerator market.
The results of this research are globally recognized for their excellence, including to be listed on the 2019 IEEE Micro Top Picks –
Honorable Mention List, which was presented in 26 of the most influential results among the hundreds of papers published in
computer system architecture in 2019.
In the meantime, this research was carried out with the support from Samsung Electronics Future Technology Foundation.
Congratulations on your achievements.
(※Source of the article : KAIST EE WEBSITE)