Sequential Printed Multilayer Perceptron Circuits for Super-TinyML Multi-Sensory Applications

Super-TinyML aims to optimize machine learning models for de-
ployment 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 sys-
tems 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 par-
allel 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 intro-
duction 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× more features
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and 65.4× more coefficients than the state of the art solutions.