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2. 2022 roadmap on neuromorphic computing and engineering

Author

Christensen, Dennis Valbjørn, Dittmann, Regina, Linares-Barranco, Bernabé, Sebastian, Abu, Le Gallo, Manuel, Redaelli, Andrea, Slesazeck, Stefan, Mikolajick, Thomas, Spiga, Sabina, Menzel, Stephan, Valov, Ilia, Milano, Gianluca, Ricciardi, Carlo, Liang, Shi-Jun, Miao, Feng, Lanza, Mario, Quill, Tyler J., Keene, Scott Tom, Salleo, Alberto, Grollier, Julie, Indiveri, Giacomo, Liu, Shih-Chii, and Donati, Elisa

Abstract

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community., Neuromorphic Computing and Engineering, 2, ISSN:2634-4386

Published
2022

6. Mechanisms for enhanced state retention and stability in redox-gated organic neuromorphic devices

Author

Keene, Scott Tom, Melianas, Armantas, van de Burgt, Yoeri, Salleo, Alberto, Keene, Scott Tom, Melianas, Armantas, van de Burgt, Yoeri, and Salleo, Alberto

Abstract

Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and electrochemical properties, opening a path toward massively parallel and energy efficient ANN implementation. However, one of the key issues with implementations relying on electrochemical gating of organic materials is the state-retention time and device stability. Here, revealed are the mechanisms leading to state loss and cycling instability in redox-gated neuromorphic devices: parasitic redox reactions and out-diffusion of reducing additives. The results of this study are used to design an encapsulation structure which shows an order of magnitude improvement in state retention and cycling stability for poly(3,4-ethylenedioxythiophene)/polyethyleneimine:poly(styrene sulfonate) devices by tuning the concentration of additives, implementing a solid-state electrolyte, and encapsulating devices in an inert environment. Finally, a comparison is made between programming range and state retention to optimize device operation.

Published
2019

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