Sequential Printed MLP Circuits for Super TinyML Multi-Sensory Applications

TL;DR

This paper introduces a novel printed electronics-based neural network architecture for super TinyML applications, enabling large, flexible, and low-power multi-sensory devices suitable for wearable and implantable tech.

Contribution

It presents a super-TinyML architecture for printed electronics that surpasses previous limits, allowing neural networks with significantly more features and parameters.

Findings

Up to 35.9 times more features than previous solutions

Up to 65.4 times more coefficients achieved

Enables large-scale neural networks on printed electronics

Abstract

Super-TinyML aims to optimize machine learning models for deployment on ultra-low-power application domains such as wearable technologies and implants. Such domains also require conformality, flexibility, and non-toxicity which traditional silicon-based systems cannot fulfill. Printed Electronics (PE) offers not only these characteristics, but also cost-effective and on-demand fabrication. However, Neural Networks (NN) with hundreds of features -- often necessary for target applications -- have not been feasible in PE because of its restrictions such as limited device count due to its large feature sizes. In contrast to the state of the art using fully parallel architectures and limited to smaller classifiers, in this work we implement a super-TinyML architecture for bespoke (application-specific) NNs that surpasses the previous limits of state of the art and enables NNs with large number of parameters. With the introduction of super-TinyML into PE technology, we address the area and power limitations through resource sharing with multi-cycle operation and neuron approximation. This enables, for the first time, the implementation of NNs with up to $35.9\times$ more features and $65.4\times$ more coefficients than the state of the art solutions.