应网络实验室邀请，8月8日（周一）下午15:00 - 16:00，香港科技大学 张骏雪 博士 给我院师生做学术报告，具体情况如下：
报告题目：LiteFlow: Towards High-performance Adaptive Neural Networks for Kernel Datapath
报告人：香港科技大学 张骏雪 博士
时间：2022年8月8号 15:00-16:00
腾讯会议 ID：121-490-362
腾讯会议入会链接：https://meeting.tencent.com/dm/vG7tn2tBXUyV
摘要：Adaptive neural networks have been used to optimize OS kernel datapath functions because they can achieve superior performance under changing environments. However, how to deploy these NNs remains a challenge. One approach is to deploy these adaptive NNs in the userspace. However, such userspace deployments suffer from either high cross-space communication overhead or low responsiveness, significantly compromising the function performance. On the other hand, pure kernel-space deployments also incur a large performance degradation because the computation logic of model tuning algorithms is complicated, interfering with the performance of normal datapath execution. This paper presents LiteFlow, a hybrid solution to build high-performance adaptive NNs for kernel datapath. At its core, LiteFlow decouples the control path of adaptive NNs into: (1) a kernel-space fast path for efficient inference execution, and (2) a userspace slow path for efficient model tuning. We have implemented LiteFlow with Linux kernel datapath and evaluated LiteFlow with three real-world datapath functions including congestion control, flow scheduling, and load balancing.
报告人简历：Junxue ZHANG is a Ph.D. candidate of computer science at iSINGLab, Hong Kong Univeristy of Science and Technology. He is under supervision of Prof. Kai CHEN. His reseach interests are data center networking, AI systems and privacy preserving computation. He is the co-founder/CTO of Clustar Technology Co., Ltd. Before joining HKUST, he received his BSc and MSc from Southeast Univeristy. He has published papers on SIGCOMM/CoNEXT.