|Peizhen Guo||Yale University|
|Bo Hu||Yale University|
|Rui Li||Yale University|
|Wenjun Hu||Yale University|
Mobile and IoT scenarios increasingly involve interactive and computation intensive contextual recognition. In this paper, the authors propose cross-device approximate computation reuse, which minimizes redundant computation .
Mobile and IoT scenarios increasingly involve interactive and computation intensive contextual recognition. Existing optimizations typically resort to computation o oading or simpli ed on-device processing. Instead, we observe that the same application is often invoked on multiple devices in close proximity. Moreover, the application instances often process similar contextual data that map to the same outcome. In this paper, we propose cross-device approximate computation reuse, which minimizes redundant computation by harnessing the “equivalence” between di erent input values and reusing previously computed outputs with high con dence. We devise adaptive locality sensitive hashing (A-LSH) and homogenized k nearest neighbors (H-kNN). The former achieves scalable and constant lookup, while the latter provides high-quality reuse and tunable accuracy guarantee. We further incorporate approximate reuse as a service, called FoggyCache, in the computation o oading runtime. Extensive evaluation shows that, when given 95% accuracy target, FoggyCache consistently harnesses over 90% of reuse opportunities, which translates to reduced computation latency and energy consumption by a factor of 3 to 10.