Swap Kernel Regression

Abstract

Recent developments in the field of artificial intelligence have increased the demand for high performance computation devices. An edge device is highly restricted not only in terms of its computational power but also memory capacity. This study proposes a method that enables both inference and learning on an edge device. The proposed method involves a kernel machine that works in restricted environments by collaborating with its secondary storage system. The kernel parameters, which are not essential for calculating the output values for the upcoming inputs, are stored in the secondary storage to make space in the main memory. The essential kernel parameters stored in the secondary storage are loaded into the main memory when required. With the use of this strategy, the system can realize the recognition/regression tasks without reducing its generalization capability.