Embedded Neuromorphic Architecture for Form + Function 4-D Printing of Robotic Materials: Emulation of Optimized Neurons

Abstract

This paper describes the optimization of a neuromorphic architecture for printable organic neurons as part of an ongoing project to develop Form + Function 4-D Printing. The previously proposed architecture prioritizes simplicity and massive redundancy for a printable analog neural network consisting of only one transistor plus memristors for synapses, per neuron, but sacrifices negative synaptic weights. This paper demonstrates an optimization technique to minimize the insertion of inverting amplifiers to realize a minimal approximating set of negative weights. This helps to develop a compact, printable and accurate neuromorphic computer to bring new function to the 3-D printing of form in multi-functional robotic materials. An example robotic skin is developed with the ability to compute the centroid of touch "compiled into the skin" to an average accuracy of 9.19% in comparison to an unconstrained Artificial Neural Network (ANN). The presented soft robotic skin is fabricated by hand using conventional silicon components, but serves as a proof-of-concept for radical new capabilities in Form + Function 4-D Printing.