NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision
Mobile vision systems usually run more than one streaming vision applications (e.g., face detection, scene understanding) at a time. Limited on-device resource incurs resource contention across applications. To this end, we propose NestDNN, a resource-aware deep learning framework. The key technique of NestDNN is a multi-capacity model that is reconfigurable according to resource variation. To fully utilize the potential of multi-capacity model, we also design a runtime scheduler that jointly optimizes the overall performance.
Paper in ACM MobiCom 2018.
Selected Media: Synced (Chinese)