Edge-Host Collaborative Deep Learning
Based on the trade-off study between energy, accuracy, and throughput of an edge platform with an embedded DNN, we are currently exploring collaborative DNN inference between the edge and the host to optimally utilize the available bandwidth and energy.
![[J27] A DNN Partitioning Framework with Controlled Lossy Mechanisms for Edge-Cloud Collaborative Intelligence](/publication/j27_fgcs_2024/featured_hufdb3eaa2451d9ab8f98ee14e248f93d3_101797_150x0_resize_q75_h2_lanczos.webp)
![[J21] A Reconfigurable Neural Architecture for Edge–Cloud Collaborative Real-Time Object Detection](/publication/j21_iot_2022/featured_hu3ce7c306b0e2767d2fec737ccbe31596_346161_150x0_resize_q75_h2_lanczos.webp)
![[C30] A Splittable DNN-Based Object Detector for Edge-Cloud Collaborative Real-Time Video Inference](/publication/c30_avss_2021/Featured_hu6aef2193e6bece41bd2a0e680fa624e7_139860_150x0_resize_q75_h2_lanczos.webp)
![[C22] Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms](/publication/c22_avss_2018/featured_hu32d1899399ed0dbf38c837842d4c5814_100818_150x0_resize_q75_h2_lanczos.webp)
![[C20] Edge-cloud collaborative processing for intelligent internet of things: a case study on smart surveillance](/publication/c20_dac_2018/featured_hu32d1899399ed0dbf38c837842d4c5814_58493_150x0_resize_q75_h2_lanczos.webp)