基本信息
卢文岩 男 硕导 中国科学院计算技术研究所
电子邮件： luwenyan@ict.ac.cn
通信地址： 北京市海淀区科学院南路6号
邮政编码：
电子邮件： luwenyan@ict.ac.cn
通信地址： 北京市海淀区科学院南路6号
邮政编码：
专利与奖励
专利成果
[1] 吴婧雅, 卢文岩, 鄢贵海, 李晓维. 一种异构系统的带宽利用率提升方法及系统. CN: CN110958183B, 2022-02-25.[2] 吴婧雅, 卢文岩, 鄢贵海, 李晓维. 一种异构系统的任务调度方法及系统. CN: CN111061547A, 2020-04-24.[3] 陆维娜, 卢文岩, 叶靖, 胡瑜, 李晓维. 一种基于可编程器件的卷积神经网络加速方法与系统. CN: CN107392308A, 2017-11-24.
出版信息
发表论文
[1] 吴婧雅, 卢文岩, 鄢贵海, 李晓维. 基于FPGA的软硬协同的多表哈希链接加速器. 高技术通讯[J]. 2023, 5: [2] 吴婧雅, 卢文岩, 鄢贵海, 李晓维. HyperTree：高并发B+树索引加速器. 计算机研究与发展[J]. 2023, 60: [3] Wenyan Lu, Yan Chen, Jingya Wu, Yu Zhang, Guihai Yan, Xiaowei Li. DOE: Database Offloading Engine for Accelerating SQL Processing. 2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW)null. 2022, [4] 鄢贵海, 卢文岩, 李晓维, 孙凝晖. 专用处理器比较分析. 中国科学:信息科学[J]. 2022, 52(2): 358-375, http://lib.cqvip.com/Qikan/Article/Detail?id=7106729241.[5] Wu, Jingya, Lu, Wenyan, Yan, Guihai, Li, Xiaowei. Portrait: A holistic computation and bandwidth balanced performance evaluation model for heterogeneous systems. SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS[J]. 2022, 35: http://dx.doi.org/10.1016/j.suscom.2022.100724.[6] 鄢贵海, 卢文岩, 李晓维, 张宇, 袁晓飞. DPU：以数据为中心的专用处理器. 中国计算机学会通讯[J]. 2021, 17(10): 51-60, [7] He, Xin, Yan, Guihai, Lu, Wenyan, Zhang, Xuan, Liu, Ke. A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks. IEEE DESIGN & TEST[J]. 2020, 37(1): 36-45, http://dx.doi.org/10.1109/MDAT.2019.2943575.[8] Wu, Jingya, Lu, Wenyan, Yan, Guihai, Li, Xiaowei, IEEE. MLA: Machine Learning Adaptation for Realtime Streaming Financial Applications. 2019 TENTH INTERNATIONAL GREEN AND SUSTAINABLE COMPUTING CONFERENCE (IGSC)null. 2019, http://apps.webofknowledge.com/CitedFullRecord.do?product=UA&colName=WOS&SID=5CCFccWmJJRAuMzNPjj&search_mode=CitedFullRecord&isickref=WOS:000533602500030.[9] Yan Guihai. A Quantitative Exploration of Collaborative Pruning and Approximation Towards Energy-Efficient Deep Neural Networks. IEEE Design & Test. 2019, [10] Gong Shijun, Li Jiajun, Lu Wenyan, Yan Guihai, Li Xiaowei, ACM. ShuntFlow: An Efficient and Scalable Dataflow Accelerator Architecture for Streaming Applications. PROCEEDINGS OF THE 2019 56TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC)null. 2019, http://dx.doi.org/10.1145/3316781.3317910.[11] Li Jiajun, Yan Guihai, Lu Wenyan, Jiang Shuhao, Gong Shijun, Wu Jingya, Yan Junchao, Li Xiaowei, ACM. TNPU: An Efficient Accelerator Architecture for Training Convolutional Neural Networks. 24TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC 2019)null. 2019, 450-455, http://dx.doi.org/10.1145/3287624.3287641.[12] Lu, Wenyan, Yan, Guihai, Li, Xiaowei. AdaFlow: Aggressive Convolutional Neural Networks Approximation by Leveraging the Input Variability. JOURNAL OF LOW POWER ELECTRONICS[J]. 2018, 14(4): 481-495, [13] He, Xin, Lu, Wenyan, Yan, Guihai, Zhang, Xuan. Joint Design of Training and Hardware Towards Efficient and Accuracy-Scalable Neural Network Inference. IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS[J]. 2018, 8(4): 810-821, http://dx.doi.org/10.1109/JETCAS.2018.2845396.[14] Li Jiajun, Yan Guihai, Lu Wenyan, Jiang Shuhao, Gong Shijun, Wu Jingya, Li Xiaowei, IEEE. CCR: A Concise Convolution Rule for Sparse Neural Network Accelerators. PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)null. 2018, 189-194, [15] Li Jiajun, Yan Guihai, Lu Wenyan, Jiang Shuhao, Gong Shijun, Wu Jingya, Li Xiaowei, IEEE. SmartShuttle: Optimizing Off-Chip Memory Accesses for Deep Learning Accelerators. PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)null. 2018, 343-348, [16] He Xin, Jiang Shuhao, Lu Wenyan, Yan Guihai, Han Yinhe, Li Xiaowei. Exploiting the Potential of Computation Reuse Through Approximate Computing. IEEE TRANSACTIONS ON MULTI-SCALE COMPUTING SYSTEMS[J]. 2017, 3(3): 152-165, [17] Lu Wenyan, Yan Guihai, Li Jiajun, Gong Shijun, Han Yinhe, Li Xiaowei, IEEE. FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks. 2017 23RD IEEE INTERNATIONAL SYMPOSIUM ON HIGH PERFORMANCE COMPUTER ARCHITECTURE (HPCA)null. 2017, 553-564, [18] 江树浩, 鄢贵海, 李家军, 卢文岩, 李晓维. 机器学习算法可近似性的量化评估分析. 计算机研究与发展[J]. 2017, 54(6): 1337-1347, http://lib.cqvip.com/Qikan/Article/Detail?id=7000282622.[19] He, Xin, Ke, Liu, Lu, Wenyan, Yan, Guihai, Zhang, Xuan. AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference. http://arxiv.org/abs/1805.08309.