Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors

Synaptic transistors have been proposed to implement
neuron activation
functions of neural networks (NNs). While promising to enable compact,
fast, inexpensive, and energy-efficient dedicated NN circuits, they
also have limitations compared to digital NNs (realized as codes for
digital processors), including shape choices of the activation function
using particular types of transistor implementation, and instabilities
due to noise and other factors present in analog circuits. We present
a computational study of the effects of these factors on NN performance
and find that, while accuracy competitive with traditional NNs can
be realized for many applications, there is high sensitivity to the
instability in the shape of the activation function, suggesting that,
when highly accurate NNs are required, high-precision circuitry should
be developed beyond what has been reported for synaptic transistors
to date.