DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.003
Publication date: 9th January 2023
The hardware implementation of neural networks is vital to realize power-efficient and fast artificial intelligence (AI) systems for a plethora of applications, e.g., bio-signal classification or real-time monitoring of industrial processes. Following the traditional paradigms of machine learning, efficient hardware implementation of AI systems requires versatile artificial synapses with well-adjustable synaptic strength, also referred to as synaptic plasticity.
However, besides these traditional approaches, it is worth taking a look at the function of biological neural networks as they possess superior properties in terms of power efficiency, fault tolerance, and flexibility. In particular, biological neural networks attain their excellent efficiency from employing specific non-linear properties of the synapses and neurons as well as of the entire system.
In this contribution, I discuss the non-linear properties of organic electrochemical transistors (OECTs), which are in the spotlight of research as they enable the hardware implementation of versatile, low-voltage artificial synapses. The non-linear properties are based on the complex interaction between polarons and ions, which can be manipulated, e.g., by chemical treatment of the semiconducting polymer. I will show how the non-linearity can be quantified and I will demonstrate three approaches to employ it for efficient computations. More specifically, I will discuss how the non-linear response of OECTs can be used to mimic the functions of biological neuron models (spiking neuron), help in decision making using Bayesian inference (stochastic computing), and classify complex bio-signals using reservoir or echo-state computing.
The author thanks the Bundesministerium für Bildung und Forschung (BMBF) for funding from the project BAYOEN (01IS21089).
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