An Adaptable Continuous Restricted Boltzmann Machine in VLSI for Fusing the Sensory Data of an Electronic Nose

Abstract

An embedded system capable of fusing sensory data is demanded for many portable or implantable microsystems. The continuous restricted Boltzmann machine (CRBM) is a probabilistic neural network not only capable of classifying data reliably but also amenable to very-large-scale-integration (VLSI) implementation. Although the embedded system based on the CRBM has been demonstrated with analog VLSI, the precision required by the learning algorithm is hardly achievable with analog circuits. Therefore, this paper investigates the feasibility of realizing the CRBM as a digital embedded system for fusing the sensory data of an electronic nose (eNose). The fusion here refers to data clustering and dimensional reduction that facilitates reliable classification. The capability of the CRBM to model different types of eNose data is first examined by MATLAB simulation. Afterward, the CRBM algorithm is customdesigned as a digital embedded system within an eNose microsystem. The functionality of the embedded CRBM system is then tested and discussed. With on-chip learning ability, the CRBM-embedded eNose is able to adapt its parameters in response to new data inputs or environmental changes.