An ultra-low power adjustable current-mode analog integrated general purpose artificial neural network classifier

Abstract

This study introduces a methodology tailored to analog hardware architecture for implementing an artificial neural network. The fundamental components of the architecture include current-mode circuits, representing the class, and a voltage-mode comparator. Specifically, the current mode circuits comprise the Mahalanobis distance circuit, Sigmoid function circuit, analog multiplier, and current mirrors. Regarding the voltage comparator, which receives the final decision, a folded-cascode operational amplifier is employed. The operational principles of the architecture are extensively explained and applied in a power-efficient configuration (operating under 976nW) with low power supply rails (0.6 V). The proposed implementation is tested on real-world biomedical classification tasks, achieving classification accuracy exceeding 91.6%. The designs are implemented using a $90\mathrm{nm}$ CMOS process and developed using the Cadence IC Suite for both schematic and layout design. Monte-Carlo analysis, encompassing both process and mismatch, as well as corner analysis, are provided to confirm the robust characteristics of the proposed classifier. Through comparative analysis of post-layout simulation results with an equivalent software-based classifier and related literature, the proper operation of the proposed architecture is confirmed.