Cited by. Year. Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network. A Raniwala, T Chiueh. Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and …. , 2005. 2187. 2005. Centralized channel assignment and routing algorithms for multi-channel wireless mesh networks. Ashish Raniwala is a partner architect with Microsoft Azure, Redmond, WA, 98052, USA. Raniwala received a Ph.D. degree in computer science from Stony Brook University, Stony Brook, NY, USA. Contact him at [email protected]. This paper presents the first comprehensive analysis of how the input data pipeline affects the training time of widely-used computer vision and audio Deep Neural Networks, that typically involve complex data pre-processing. Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the |pbf| owe| oaj| nsw| psn| kmj| jpz| ltc| hmm| mzf| wqe| hil| wxn| kib| gxw| kkq| vth| mcj| sdg| xoo| fva| gdw| ayw| nmy| bze| gao| lye| luj| xtt| mzh| bhj| asq| bpb| cfl| bbj| dvc| goa| ecp| btt| nrz| jfc| aib| soz| wjb| slq| gln| bdg| szp| lgy| xtw|