Modular Modeling of Analog Organic Neuromorphic Circuits: Toward Prototyping of Hardware-Level Spiking Neural Networks

Yi Yang^a, Mohammad Javad Mirshojaeian Hosseini^a, Walter Kruger^b, Robert Nawrocki^a

^aPurdue University, West Lafayette, IN, 47907, USA

^bCisco System

Proceedings of Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCaS)

VALÈNCIA, Spain, 2023 January 23rd - 25th

Organizers: Rohit Abraham John, Irem Boybat, Jason Eshraghian and Simone Fabiano

Contributed talk, Yi Yang, presentation 047
DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.047
Publication date: 9th January 2023

While silicon-based neuromorphic circuits (Si-NCs) offer the advantages of small-scale and real-time system-on-chip computing, the complex manufacturing process of Si-NCs often requires expensive high-precision equipment operating in ultra-high temperature and ultra-clean facilities.¹ Besides, declining performance of Si-NCs has been reported in applications requiring biocompatibility, physical flexibility, and large area coverage of biological tissues.^2–4 As organic counterpart of Si-NCs, the organic neuromorphic circuits (ONCs) consisting of Organic Field Effect Transistors (OFETs) are fast becoming popular due to their unparalleled processing conditions and low manufacturing cost.^2,5,6 However, the low reproducibility, low state retention, low temperature stability, high write/read speed and high switching noise of hardware implementations have made the design and fabrication of ONCs unpredictable and inefficient.

We report on a novel modeling approach for analog organic circuits using very simple to customize circuit topology and parameters of individual p- and n-type OFETs.⁷ Aided with the combination of primitive elements (OFETs, capacitors, resistors), the convoluted behavior of analog ONCs and other general analog organic circuits, can be predicted. The organic log-domain integrator (oLDI⁸) synaptic circuit, the organic differential-pair integrator (oDPI⁹) synaptic circuit, and the organic Axon-Hillock (oAH¹⁰) somatic circuit are designed and serve as the modular circuit primitives of more complicated ONCs. We first validate our modeling approach by comparing the simulated oDPI and oAH circuit responses to their experimental measurements. Thereafter, the summation effects of the excitatory and inhibitory oDPI circuits in prototyped ONCs are investigated. We also predict the dynamic power dissipation of modular ONCs and show an average power consumption of 2.1 μJ per spike for the oAH soma at a ~1 Hz spiking frequency. Furthermore, we compare our modeling approach with other two representative organic circuit models and prove that our approach outperforms the other two in terms of accuracy and convergence speed. This work pioneers the study of ONC and organic analog circuit modeling and will help improve the design efficiency of larger-scale hardware-level SNNs.

References:

[1] Tuchman, Y. et al. Organic neuromorphic devices: Past, present, and future challenges. MRS Bull. 45, 619–630 (2020).

[2] Van De Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. & Salleo, A. Organic electronics for neuromorphic computing. Nat. Electron. 1, 386–397 (2018).

[3] Lee, Y. & Lee, T.-W. W. Organic Synapses for Neuromorphic Electronics: From Brain-Inspired Computing to Sensorimotor Nervetronics. Acc. Chem. Res. 52, 964–974 (2019).

[4] Keene, S. T., Gkoupidenis, P. & Burgt, Y. van de. Neuromorphic computing systems based on flexible organic electronics. Org. Flex. Electron. 531–574 (2021).

[5] Sarkar, T., Lieberth, K., Pavlou, A. et al. An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing. Nat Electron 5, 774–783 (2022).

[6] Harikesh, P.C., Yang, CY., Tu, D. et al. Organic electrochemical neurons and synapses with ion mediated spiking. Nat Commun 13, 901 (2022).

[7] Yang, Y., Javad, M., Hosseini, M., Kruger, W. & Nawrocki, R. A. Modular Modeling of Analog Organic Neuromorphic Circuits : Towards Prototyping of Hardware-Level Spiking Neural Networks. IEEE Trans. Circuits Syst. I Regul. Pap. (2022).

[8] Hosseini, M. J. M., Donati, E., Indiveri, G., Nawrocki, R. A., Organic Log-Domain Integrator Synapse. Adv. Electron. Mater. 2022, 8, 2100724.

[9] Mohammad Javad Mirshojaeian Hosseini, et al., 2022, Neuromorph. Comput. Eng., 2, 034009.

[10] Hosseini, M. J. M. et al. Organic electronics Axon-Hillock neuromorphic circuit: towards biologically compatible, and physically flexible, integrate-and-fire spiking neural networks. J. Phys. D. Appl. Phys. 54, 104004 (2020)

Acknowledgements:

The work was partially sponsored by the Office of Naval Research Young Investigator Program, Award No.: N00014-21-1-2585, and Purdue Polytechnic Institute's graduate fellowship. The authors would also like to acknowledge the help of Prof. Saeed Mohammadi and Prof. Walter Daniel Leon-Salas, from Purdue University, for fruitful discussions.

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