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