Toward Data-Adaptable TinyML using Model Partial Replacement for Resource Frugal Edge Device
Jisu Kwon, Daejin Park · 2021
Demand to perform machine learning (ML) tasks in microcontroller unit (MCU)-based edge devices instead of the server, that have limited resources, is gradually increasing. TinyML framework makes possible that creating ML firmware in a language that can be ported to the MCU. This paper aims at a technique that flexibly responds to various inputs by partial replacement of the network model part among the ML firmware operating in the MCU. Before implementing the proposed technique, a preliminary experiment was performed. As the number of words trained on the network in the speech command dataset increases, the size of the model increases, but the evaluation accuracy decreases. The experimental results show the possibility of a technique that replaces small learning models corresponded to each domain, instead of using a huge model that trains all input data variations for different domains.