 코드번호 : |
2 |
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| 발표자 : |
윤성로 |
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| 소속 : |
서울대 |
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| 부서 : |
전기정보공학부 |
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| 직위 : |
교수 |
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| 세션시간 : |
16:00~18:00 |
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| 발표자약력 : |
2012-현재: 서울대학교 전기정보공학부 교수
2007-2012: 고려대학교 전기전자공학부 교수
2006-2007: 미국 Intel 선임연구원
2006-2006: 미국 스탠퍼드대학교 박사후 연구원
2006. 미국 스탠퍼드대학교 박사
1996. 서울대학교 학사 |
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| 강연요약 : |
The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that simultaneously exploits Apache Spark for large-scale distributed data management and Caffe for GPU-based acceleration. DeepSpark directly accepts Caffe input specifications, providing seamless compatibility with existing designs and network structures. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe-running nodes using Spark and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent (SGD) update scheme, effectively complementing the synchronized processing capabilities of Spark. DeepSpark is an on-going project, and the current release is available at http://deepspark.snu.ac.kr. |
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