Reminding forgetful organic neuromorphic device networks

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.

Spiking neural networks compensate for weight drift in organic neuromorphic device networks

“…Additional compensation mechanisms should be considered if higher idle rates are expected for long periods. One such mechanism [17] is based on reminder pulses that are computed from each device's current state and the time of self-discharge based on a device model. They were shown to restore a rate-based ANNs state and accuracy.…”

“…To guarantee a stable baseline, the two devices are combined into a differential synapse [17,27] (figure 1(C)) with the weight w defined as,…”

“…This effect can be partially mitigated by careful device design [16] but gets worse for small, scaled-down devices [7] that are required for many applications. Therefore, the weights of such an organic neural network degrade and information and trained behavior are lost over time in as little as a few minutes [17]. This effect is hard to eliminate and requires careful algorithm-hardware co-design to keep the neural network operational over longer time periods.…”

“…This effect is hard to eliminate and requires careful algorithm-hardware co-design to keep the neural network operational over longer time periods. In a previous effort [17], a compensation mechanism was introduced to allow rate-based artificial neural networks (ANNs) to operate on forgetful organic neuromorphic devices for extended periods of time. However, an ideal system would reduce additional circuitry and complexity as much as possible, i.e.…”

Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications

“…Despite recent advantages for organic neuromorphic systems for in memory computing, one of the main challenges remains the stability and in particular the state retention. In our special issue Felder et al [1] introduce a method to overcome this issue by 'reminding' the neuromorphic devices at certain times. This way the classification accuracy can remain high with a simple action.…”