Integrated architecture of ITCAM and reservoir computing for time series data

Abstract

This paper presents a new architecture to give personality to robots and artificial intelligence (AI). Communication robots are used for elderly care and as AI assistants. However, the currently available communication robots can only perform general actions and have limitations in following instructions. In this study, we developed a new architecture that combines Ternary Content Addressable Memory with Individuality (ITCAM) and reservoir computing, which reflects chip variations in search results. This architecture is used to perform learning and inference by using the weights of nine field-programmable gate array chips of the same standard. In experiments using sine waves and triangular waves, the individuality range of each chip performed within a 5% range. This architecture allows individual chips to vary their predictions while referring to teaching data. Furthermore, experiments using atmospheric temperature show the promise that an ITCAM-based Reservoir Computing Architecture (IRC) approach can be used to predict the actual conditions in the volatile real world. Even in years where the normalized average temperature changed significantly including cases with variances of more than 10% per day compared to previous years, the IRC model can generate results within a range of 4%.