Poster: Approximate Caching for Mobile Image Recognition
James Mariani, Yongqi Han, Li Xiao · 2021
Many emerging mobile applications rely heavily upon image recognition of both static images and live video streams. Image recognition is commonly achieved using deep neural networks (DNNs) which can achieve high accuracy but also incur significant computation latency and energy consumption on resource-constrained smartphones. We introduce an in-memory caching paradigm that supports infrastructure-less collaborative computation reuse in smartphone image recognition. We propose using the inertial movement of smartphones, the locality inherent in video streams, as well as information from nearby, peer-to-peer devices to maximize the computation reuse opportunities in mobile image recognition. Experimental results show that our system lowers the average latency of standard mobile neural network image recognition applications by up to 94% with minimal loss of recognition accuracy.