Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

Biologically-inspired neuromorphic computing paradigms are computational platforms that imitate synaptic and neuronal activities in the human brain to process big data flows in an efficient and cognitive manner. In the past decades, neuromorphic computing has been widely investigated in various application fields such as language translation, image recognition, modeling of phase, and speech recognition, especially in neural networks (NNs) by utilizing emerging nanotechnologies; due to their inherent miniaturization with low power cost, they can alleviate the technical barriers of neuromorphic computing by exploiting traditional silicon technology in practical applications. In this work, we review recent advances in the development of brain-inspired computing (BIC) systems with respect to the perspective of a system designer, from the device technology level and circuit level up to the architecture and system levels. In particular, we sort out the NN architecture determined by the data structures centered on big data flows in application scenarios. Finally, the interactions between the system level with the architecture level and circuit/device level are discussed. Consequently, this review can serve the future development and opportunities of the BIC system design

Memristors

This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now 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 10$^{18}$ 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

Fabrication and Application of a Polymer Neuromorphic Circuitry Based on Polymer Memristive Devices and Polymer Transistors

Neuromorphic engineering is a discipline that aims to address the shortcomings of today\u27s serial computers, namely large power consumption, susceptibility to physical damage, as well as the need for explicit programming, by applying biologically-inspired principles to develop neural systems with applications such as machine learning and perception, autonomous robotics and generic artificial intelligence. This doctoral dissertation presents work performed fabricating a previously developed type of polymer neuromorphic architecture, termed Polymer Neuromorphic Circuitry (PNC), inspired by the McCulloch-Pitts model of an artificial neuron. The major contribution of this dissertation is a development of processing techniques necessary to realize the Polymer Neuromorphic Circuitry, which required a development of individual polymer electronics elements, as well as customization of fabrication processes necessary for the realization of the circuitry on separate substrates as well as on a single substrate. This is the first demonstration of a fabrication of an entire neuron, and more importantly, a network of such neurons, that includes both the weighting functionality of a synapse and the somatic summing, all realized with polymer electronics technology. Polymer electronics is a new branch of electronics that is based on conductive and semi-conductive polymers. These new elements hold a great advantage over the conventional, inorganic electronics in the form of physical flexibility, low cost and ease of fabrication, manufacturing compatibility with many substrate materials, as well as greater biological compatibility. These advantages were the primary motivation for the choice to fabricate all of the electrical components required to realize the PNC, namely polymer transistors, polymer memristive devices, and polymer resistors, with polymer electronics components. The efficacy of this design is validated by demonstrating that the activation function of a single neuron approximates the sigmoidal function commonly employed by artificial neural networks. The utility of the neuromorphic circuitry is further corroborated by illustrating that a network of such neurons, and even a single neuron, are capable of performing linear classification for a real-life problem

Dynamics meets Morphology: towards Dymorph Computation

In this dissertation, approaches are presented for both technically using and investigating biological principles with oscillators in the context of electrical engineering, in particular neuromorphic engineering. Thereby, dynamics as well as morphology as important neuronal principles were explicitly selected, which shape the information processing in the human brain and distinguish it from other technical systems. The aspects and principles selected here are adaptation during the encoding of stimuli, the comparatively low signal transmission speed, the continuous formation and elimination of connections, and highly complex, partly chaotic, dynamics. The selection of these phenomena and properties has led to the development of a sensory unit that is capable of encoding mechanical stress into a series of voltage pulses by the use of a MOSFET augmented by AlScN. The circuit is based on a leaky integrate and fire neuron model and features an adaptation of the pulse frequency. Furthermore, the slow signal transmission speed of biological systems was the motivation for the investigation of a temporal delay in the feedback of the output pulses of a relaxation oscillator. In this system stable pulse patterns which form due to so-called jittering bifurcations could be observed. In particular, switching between different stable pulse patterns was possible to induce. In the further course of the work, the first steps towards time-varying coupling of dynamic systems are investigated. It was shown that in a system consisting of dimethyl sulfoxid and zinc acetate, oscillators can be used to force the formation of filaments. The resulting filaments then lead to a change in the dynamics of the oscillators. Finally, it is shown that in a system with chaotic dynamics, the extension of it with a memristive device can lead to a transient stabilisation of the dynamics, a behaviour that can be identified as a repeated pass of Hopf bifurcations