- 세미나 [11/26] Machine Learning in Silicon
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< BK21 플러스 BEST 정보기술 사업단 세미나 개최 안내 >
개최일시 : 2018 년 11월 26일 (월) 16:00 ~ 17:00
개최장소 : 제2공학관 B201호
세미나 제목 : Machine Learning in Silicon
There is much interest in embedding data analytics and artificial intelligence capabilities into sensor-rich platforms such as wearables, biomedical devices, autonomous vehicles, robots, and the Internet of Things (IoT) to provide them with decision-making capabilities. Such platforms need to implement machine learning algorithms under severe resource constraints on embedded battery-powered platforms. On the other hand, such ML algorithms require processing of large data volumes, e.g., >100 M neurons for VGGNet.
As a result, there have been on-going effort to realize energy- and latency- efficient information processing. In this talk, recent research trend in machine learning accelerator architecture will be discussed including algorithm, circuit, and architecture level innovations. As an example of such implementation, deep in-memory architecture will be described in further detail by focusing its concept, design principles, challenges, and silicon-measured results.
강연자 성함&직함 / 소속 : 강민구, Research Staff member / IBM TJ Watson Research Center
초청자 : 전기전자공학과 교수 정성욱