Multithreading Incremental Learning Scheme for Embedded System to Realize a High-Throughput

Abstract

Recent improvement of the microcomputer enables it to execute complex intelligent algorithms on embedded systems. However, when using conventional incremental learning methods, its resources are often increased with learning, and continuing the execution of the incremental learning becomes difficult on small embedded systems. Moreover, for real applications, the response time should be reduced. This paper proposes a technique for implementing incremental learning methods on a budget. Normally, they proceed online learning by alternating recognition and learning, so that they cannot respond to the next new instance until the previous learning is finished. Unfortunately, their computational learining complexities are extremely high to realize a quick response to new input. Therefore, this paper introduces a multithreading technique for such learning schemes. The recognition and learning threads are executed in parallel so that the system can respond to a new instance even when it is in the progress of learning. Moreover, this paper shows that such multithreading learning schemes sometime need a “sleep-period” to complete the learning similar to a biological brain. During the “sleep-period,” the leaning system prohibits the receival of any sensory inputs and yielding outputs.