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Mixed-signal machine-learning classification has recently been demonstratedas an efficient alternative for classification with power expensive digitalcircuits. In this paper, a high-COnfidence high-REsolution (CORE) mixed-signalclassifier is proposed for classifying high-dimensional input data intomulti-class output space with less power and area than state-of-the-artclassifiers. A high-resolution multiplication is facilitated within asingle-MOSFET by feeding the features and feature weights into, respectively,the body and gate inputs. High-resolution classifier that considers theconfidence of the individual predictors is designed at 45 nm technology nodeand operates at 100 MHz in subthreshold region. To evaluate the performance ofthe classifier, a reduced MNIST dataset is generated by downsampling the MNISTdigit images from 28 $\times$ 28 features to 9 $\times$ 9 features. The systemis simulated across a wide range of PVT variations, exhibiting nominal accuracyof 90%, energy consumption of 6.2 pJ per classification (over 45 times lowerthan state-of-the-art classifiers), area of 2,179 $\mu$$m^{2}$ (over 7.3 timeslower than state-of-the-art classifiers), and a stable response under PVTvariations.

A Single-MOSFET MAC for Confidence and Resolution (CORE) Driven Machine Learning Classification