Reminding forgetful organic neuromorphic device networks

Neuromorphic Computing and Engineering, vol. 2, pp. 044014

Abstract

Abstract Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high switching speeds and low energy demand. However, as electrochemical systems, they are prone to self-discharge through parasitic electrochemical reactions. Therefore, the network’s synapses forget their trained conductance states over time. This work integrates single-device high-resolution charge transport models to simulate entire neuromorphic device networks and analyze the impact of self-discharge on network performance. Simulation of a single-layer nine-pixel image classification network commonly used in experimental demonstrations reveals no significant impact of self-discharge on training efficiency. And, even though the network’s weights drift significantly during self-discharge, its predictions remain 100% accurate for over ten hours. On the other hand, a multi-layer network for the approximation of the circle function is shown to degrade significantly over twenty minutes with a final mean-squared-error loss of 0.4. We propose to counter the effect by periodically reminding the network based on a map between a synapse’s current state, the time since the last reminder, and the weight drift. We show that this method with a map obtained through validated simulations can reduce the effective loss to below 0.1 even with worst-case assumptions. Finally, while the training of this network is affected by self-discharge, a good classification is still obtained. Electrochemical organic neuromorphic devices have not been integrated into larger device networks. This work predicts their behavior under nonideal conditions, mitigates the worst-case effects of parasitic self-discharge, and opens the path toward implementing fast and efficient neural networks on organic neuromorphic hardware.

Authors 4

1. Daniel Felder Aachen AVT.CVT.- Chair of Chemical Process Engineering DWI - Leibniz Institute for Interactive Materials

   RWTH Aachen University · DWI – Leibniz Institute for Interactive Materials

   Affiliation as printed

   AVT.CVT - Chair of Chemical Process Engineering, RWTH Aachen University, Forckenbeckstrasse 51, Aachen, Germany

   DWI - Leibniz Institute for Interactive Materials, Forckenbeckstrasse 50, Aachen, Germany

2. Katerina Muche Aachen AVT.CVT.- Chair of Chemical Process Engineering

   RWTH Aachen University

   Affiliation as printed

   AVT.CVT - Chair of Chemical Process Engineering, RWTH Aachen University, Forckenbeckstrasse 51, Aachen, Germany

3. John Linkhorst Aachen AVT.CVT.- Chair of Chemical Process Engineering

   RWTH Aachen University

   Affiliation as printed

   AVT.CVT - Chair of Chemical Process Engineering, RWTH Aachen University, Forckenbeckstrasse 51, Aachen, Germany

4. Matthias Weßling corresponding Aachen AVT.CVT.- Chair of Chemical Process Engineering DWI - Leibniz Institute for Interactive Materials

   RWTH Aachen University · DWI – Leibniz Institute for Interactive Materials

   Affiliation as printed

   AVT.CVT - Chair of Chemical Process Engineering, RWTH Aachen University, Forckenbeckstrasse 51, Aachen, Germany

   DWI - Leibniz Institute for Interactive Materials, Forckenbeckstrasse 50, Aachen, Germany