Journal articles: 'Neuromorphic platform'

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Urgese, Gianvito, Francesco Barchi, Emanuele Parisi, Evelina Forno, Andrea Acquaviva, and Enrico Macii. "Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm." Electronics 8, no. 11 (November 14, 2019): 1342. http://dx.doi.org/10.3390/electronics8111342.

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SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform.

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Perez-Peña, Fernando, M. Angeles Cifredo-Chacon, and Angel Quiros-Olozabal. "Digital neuromorphic real-time platform." Neurocomputing 371 (January 2020): 91–99. http://dx.doi.org/10.1016/j.neucom.2019.09.004.

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Russo, Nicola, Haochun Huang, Eugenio Donati, Thomas Madsen, and Konstantin Nikolic. "An Interface Platform for Robotic Neuromorphic Systems." Chips 2, no. 1 (February 1, 2023): 20–30. http://dx.doi.org/10.3390/chips2010002.

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Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a communication protocol that allows communication between different devices, using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) links different devices as a whole system and provides a power supply for the entire system using batteries as the power supply. Concretely, we have connected a Dynamic Vision Sensor (DVS128), SpiNNaker board and a servo motor, creating a platform for a neuromorphic robotic system controlled by a Spiking Neural Network, which is demonstrated on the task of intercepting incoming objects. The data rate of the implemented interface board is 24.64 k symbols/s and the latency for generating commands is about 11ms. The complete system is run only by batteries, making it very suitable for robotic applications.

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Wang, Junyi. "A Review of Spiking Neural Networks." SHS Web of Conferences 144 (2022): 03004. http://dx.doi.org/10.1051/shsconf/202214403004.

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Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages in Spatio-temporal dynamics, diverse coding mechanisms, and event-driven properties. This paper is a review of SNN in order to help researchers from other areas to know and became familiar with the field of SNN or even became interested in SNN. Neuron models, coding methods, training algorithms, and neuromorphic computing platforms will be introduced in this paper. This paper analyzes the disadvantages and advantages of several kinds of neural models, coding methods, learning algorithms, and neuromorphic computing platforms, and according to these to propose some expected development, such as improving the balance between bio-mimicry and cost of computing for neuron models, compounding coding methods, unsupervised learning algorithms in SNN, and digital-analog computing platform.

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Zhai, Yongbiao, Peng Xie, Jiahui Hu, Xue Chen, Zihao Feng, Ziyu Lv, Guanglong Ding, Kui Zhou, Ye Zhou, and Su-Ting Han. "Reconfigurable 2D-ferroelectric platform for neuromorphic computing." Applied Physics Reviews 10, no. 1 (March 2023): 011408. http://dx.doi.org/10.1063/5.0131838.

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To meet the requirement of data-intensive computing in the data-explosive era, brain-inspired neuromorphic computing have been widely investigated for the last decade. However, incompatible preparation processes severely hinder the cointegration of synaptic and neuronal devices in a single chip, which limited the energy-efficiency and scalability. Therefore, developing a reconfigurable device including synaptic and neuronal functions in a single chip with same homotypic materials and structures is highly desired. Based on the room-temperature out-of-plane and in-plane intercorrelated polarization effect of 2D α-In2Se3, we designed a reconfigurable hardware platform, which can switch from continuously modulated conductance for emulating synapse to spiking behavior for mimicking neuron. More crucially, we demonstrate the application of such proof-of-concept reconfigurable 2D ferroelectric devices on a spiking neural network with an accuracy of 95.8% and self-adaptive grow-when required network with an accuracy of 85% by dynamically shrinking its nodes by 72%, which exhibits more powerful learning ability and efficiency than the static neural network.

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Boldman, Walker L., Cheng Zhang, Thomas Z. Ward, Dayrl P. Briggs, Bernadeta R. Srijanto, Philip Brisk, and Philip D. Rack. "Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform." Micromachines 10, no. 7 (July 17, 2019): 478. http://dx.doi.org/10.3390/mi10070478.

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Due to the limit in computing power arising from the Von Neumann bottleneck, computational devices are being developed that mimic neuro-biological processing in the brain by correlating the device characteristics with the synaptic weight of neurons. This platform combines ionic liquid gating and electrowetting for programmable placement/connectivity of the ionic liquid. In this platform, both short-term potentiation (STP) and long-term potentiation (LTP) are realized via electrostatic and electrochemical doping of the amorphous indium gallium zinc oxide (aIGZO), respectively, and pulsed bias measurements are demonstrated for lower power considerations. While compatible with resistive elements, we demonstrate a platform based on transitive amorphous indium gallium zinc oxide (aIGZO) pixel elements. Using a lithium based ionic liquid, we demonstrate both potentiation (decrease in device resistance) and depression (increase in device resistance), and propose a 2D platform array that would enable a much higher pixel count via Active Matrix electrowetting.

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Tang, Jianbin, Benjamin Scott Mashford, and Antonio Jimeno Yepes. "Semantic Labeling Using a Low-Power Neuromorphic Platform." IEEE Geoscience and Remote Sensing Letters 15, no. 8 (August 2018): 1184–88. http://dx.doi.org/10.1109/lgrs.2018.2834522.

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Bose, Saurabh K., Joshua B. Mallinson, Edoardo Galli, Susant K. Acharya, Chloé Minnai, Philip J. Bones, and Simon A. Brown. "Neuromorphic behaviour in discontinuous metal films." Nanoscale Horizons 7, no. 4 (2022): 437–45. http://dx.doi.org/10.1039/d1nh00620g.

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Discontinuous metal films, comprising nanoscale gold islands, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex, providing an interesting platform for brain-inspired computing.

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Sugiarto, Indar, and Felix Pasila. "Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform." MATEC Web of Conferences 164 (2018): 01015. http://dx.doi.org/10.1051/matecconf/201816401015.

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Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied for general tasks? This question motivates the emergence of another research discipline: neuromorphic computing (NC). In NC, researchers try to identify the most fundamental ingredients that construct intelligence behaviour produced by the brain itself. To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level. In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach. This paper is a literature review that contains comparative study on algorithms that have been implemented in SpiNNaker.

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Petrov, A., L. Alekseeva, A. Ivanov, V. Luchinin, A. Romanov, T. Chikyow, and T. Nabatame. "On the way to a neuromorphic memristor computer platform." Nanoindustry Russia, no. 1 (2016): 94–109. http://dx.doi.org/10.22184/1993-8578.2016.63.1.94.109.

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Ghani, Arfan, Thomas Dowrick, and Liam J. McDaid. "OSPEN: an open source platform for emulating neuromorphic hardware." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (March 1, 2023): 1. http://dx.doi.org/10.11591/ijres.v12.i1.pp1-8.

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This paper demonstrates a framework that entails a bottom-up approach to accelerate research, development, and verification of neuro-inspired sensing devices for real-life applications. Previous work in neuromorphic engineering mostly considered application-specific designs which is a strong limitation for researchers to develop novel applications and emulate the true behaviour of neuro-inspired systems. Hence to enable the fully parallel brain-like computations, this paper proposes a methodology where a spiking neuron model was emulated in software and electronic circuits were then implemented and characterized. The proposed approach offers a unique perspective whereby experimental measurements taken from a fabricated device allowing empirical models to be developed. This technique acts as a bridge between the theoretical and practical aspects of neuro-inspired devices. It is shown through software simulations and empirical modelling that the proposed technique is capable of replicating neural dynamics and post-synaptic potentials. Retrospectively, the proposed framework offers a first step towards open-source neuro-inspired hardware for a range of applications such as healthcare, applied machine learning and the internet of things (IoT).

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Bradley, H., S. Louis, C. Trevillian, L. Quach, E. Bankowski, A. Slavin, and V. Tyberkevych. "Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing." AIP Advances 13, no. 1 (January 1, 2023): 015206. http://dx.doi.org/10.1063/5.0128530.

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Spiking artificial neurons emulate the voltage spikes of biological neurons and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to construct spiking artificial neurons. When configured as a neuron, the magnetization in antiferromagnetic materials has an effective inertia that gives them intrinsic characteristics that closely resemble biological neurons, in contrast with conventional artificial spiking neurons. It is shown here that antiferromagnetic neurons have a spike duration on the order of picoseconds, a power consumption of about 10−3 pJ per synaptic operation, and built-in features that directly resemble biological neurons, including response latency, refraction, and inhibition. It is also demonstrated that antiferromagnetic neurons interconnected into physical neural networks can perform unidirectional data processing even for passive symmetrical interconnects. The flexibility of antiferromagnetic neurons is illustrated by simulations of simple neuromorphic circuits realizing Boolean logic gates and controllable memory loops.

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Vanarse, Anup, Adam Osseiran, Alexander Rassau, and Peter van der Made. "Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts." Sensors 22, no. 2 (January 7, 2022): 440. http://dx.doi.org/10.3390/s22020440.

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Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

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Devyatisil’nyi, A. S. "System for neuromorphic estimation of rotation of a mobile technological platform." Technical Physics 58, no. 7 (July 2013): 946–49. http://dx.doi.org/10.1134/s1063784213070050.

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Kösters, Dominique J., Bryan A. Kortman, Irem Boybat, Elena Ferro, Sagar Dolas, Roberto Ruiz de Austri, Johan Kwisthout, et al. "Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics." APL Machine Learning 1, no. 1 (March 1, 2023): 016101. http://dx.doi.org/10.1063/5.0116699.

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The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here, we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two-dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware.

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Ferreira de Lima, Thomas, Alexander N. Tait, Armin Mehrabian, Mitchell A. Nahmias, Chaoran Huang, Hsuan-Tung Peng, Bicky A. Marquez, et al. "Primer on silicon neuromorphic photonic processors: architecture and compiler." Nanophotonics 9, no. 13 (August 10, 2020): 4055–73. http://dx.doi.org/10.1515/nanoph-2020-0172.

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AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

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Kim, Jaeseop, Seungyeon Lee, and Jiman Hong. "Reduction of Inference time in Neuromorphic Based Platform for IoT Computing Environments." Korean Institute of Smart Media 11, no. 2 (March 30, 2022): 77–83. http://dx.doi.org/10.30693/smj.2022.11.2.77.

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Devyatisil’nyi, A. S. "Neuromorphic expansion of the GLONASS onboard functions for a mobile technological platform." Technical Physics 60, no. 10 (October 2015): 1419–22. http://dx.doi.org/10.1134/s1063784215100114.

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Singh, Jagmeet, Hugh Morison, Zhimu Guo, Bicky A. Marquez, Omid Esmaeeli, Paul R. Prucnal, Lukas Chrostowski, Sudip Shekhar, and Bhavin J. Shastri. "Neuromorphic photonic circuit modeling in Verilog-A." APL Photonics 7, no. 4 (April 1, 2022): 046103. http://dx.doi.org/10.1063/5.0079984.

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One of the significant challenges in neuromorphic photonic architectures is the lack of good tools to simulate large-scale photonic integrated circuits. It is crucial to perform simulations on a single platform to capture the circuit’s behavior in the presence of both optical and electrical components. Here, we adopted a Verilog-A based approach to model neuromorphic photonic circuits by considering both the electrical and optical properties. Verilog-A models for the primary optical devices, such as lasers, couplers, waveguides, phase shifters, and photodetectors, are discussed, along with studying the composite devices such as microring resonators. Model parameters for different optical devices are extracted and tuned by analyzing the measured data. The simulated and experimental results are also compared for validation of Verilog-A models. Finally, a single photonic neuron circuit is simulated by implementing input, weight, and non-linear activation function by using lasers, microring resonators, and modulator, respectively. Electro-optical rapid co-simulation would significantly improve the efficiency of optimizing the devices and provide an accurate simulation of the circuit performance.

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Forno, Evelina, Alessandro Salvato, Enrico Macii, and Gianvito Urgese. "PageRank Implemented with the MPI Paradigm Running on a Many-Core Neuromorphic Platform." Journal of Low Power Electronics and Applications 11, no. 2 (May 28, 2021): 25. http://dx.doi.org/10.3390/jlpea11020025.

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SpiNNaker is a neuromorphic hardware platform, especially designed for the simulation of Spiking Neural Networks (SNNs). To this end, the platform features massively parallel computation and an efficient communication infrastructure based on the transmission of small packets. The effectiveness of SpiNNaker in the parallel execution of the PageRank (PR) algorithm has been tested by the realization of a custom SNN implementation. In this work, we propose a PageRank implementation fully realized with the MPI programming paradigm ported to the SpiNNaker platform. We compare the scalability of the proposed program with the equivalent SNN implementation, and we leverage the characteristics of the PageRank algorithm to benchmark our implementation of MPI on SpiNNaker when faced with massive communication requirements. Experimental results show that the algorithm exhibits favorable scaling for a mid-sized execution context, while highlighting that the performance of MPI-PageRank on SpiNNaker is bounded by memory size and speed limitations on the current version of the hardware.

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Zhang, Zhen, Yifei Sun, and Hai-Tian Zhang. "Quantum nickelate platform for future multidisciplinary research." Journal of Applied Physics 131, no. 12 (March 28, 2022): 120901. http://dx.doi.org/10.1063/5.0084784.

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Perovskite nickelates belong to a family of strongly correlated materials, which have drawn broad attention due to their thermally induced metal-to-insulator transition. Recent discoveries show that orbital filling mediated by ion intercalation can trigger a colossal non-volatile conductivity change in nickelates. The coupling and interaction between two types of charge carriers (i.e., ions and electrons) enable nickelate as an exotic mixed conductor for electronic, biological, and energy applications. In this Perspective, we first summarize the fundamentals and recent progresses in the manipulation of ground states of perovskite nickelates by controlling orbital filling via ion intercalation. Then, we present a comprehensive overview of perovskite nickelate as a unique platform for vast cutting-edge research fields, including neuromorphic computing, bio-electronic interfaces, as well as electrocatalysis applications by taking advantage of such electron-filling-controlled modulation phenomena. Finally, we provide an overview of future perspectives and remaining challenges toward the exploitation and commercialization of quantum nickelates for future multidisciplinary research.

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Liu, Y. H., L. Chen, X. W. Li, Y. C. Wu, S. Liu, J. J. Wang, S. G. Hu, Q. Yu, T. P. Chen, and Y. Liu. "Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform." AIP Advances 12, no. 3 (March 1, 2022): 035106. http://dx.doi.org/10.1063/5.0075761.

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Epilepsy is a serious neurological condition caused by a sudden abnormality of brain neurons. An accurate epilepsy detection based on electroencephalogram (EEG) signals can provide vital information for diagnosis and treatment. In this study, we propose a lightweight automatic epilepsy detection system with artificial neural network based on our as-fabricated neuromorphic chip. The proposed system utilizes a neural network model to achieve high-accuracy detection without the need for epilepsy-related prior knowledge. The model uses a filter module and a convolutional neural network to preprocess the raw EEG signal and uses a long short-term memory recurrent neural network and a fully connected network as the classifier. In the examination, the classification accuracy of the normal cases and seizures approaches 99.10%, and the accuracy of the normal cases, and interictal and seizure cases can reach 94.46%. This design provides possible epilepsy detection in wearable or portable devices.

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Han, Xu, Yimeng Xu, Bowen Sun, Ruixue Xu, Jing Xu, Wang Hong, Zhiwei Fu, et al. "Highly transparent flexible artificial nociceptor based on forming-free ITO memristor." Applied Physics Letters 120, no. 9 (February 28, 2022): 094103. http://dx.doi.org/10.1063/5.0082538.

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Designing a flexible transparent electronic device with biological functions is of great interest for the future wearable integrated artificial intelligence equipment. Nociceptor is a vitally important receptor of sensory neuron, which is responsible for providing a warning signal by recognizing noxious stimuli to reduce potential physical injury. Here, a flexible transparent artificial nociceptor device is demonstrated to simulate the biological nociceptor functions based on the indium tin oxide (ITO) memristor, which exhibits forming-free and reproducible threshold resistive switching behaviors. This structurally simple memristor can imitate the key features of biological nociceptor, including “threshold,” “relaxation,” and “no adaptation” behaviors and sensitization phenomena of hyperalgesia and allodynia upon external stimuli. Finally, an alarm system is built to demonstrate the simplicity and feasibility of this artificial nociceptor for future neuromorphic systems. These results indicate a potential application of the ITO memristor in the future flexible invisible neuromorphic cognitive platform.

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Chaudhary, Mayur, and Yu-Lun Chueh. "Dual Threshold and Memory Switching Induced By Conducting Filament Morphology in Ag/WSe₂ Based ECM Cell." ECS Meeting Abstracts MA2022-02, no. 36 (October 9, 2022): 1334. http://dx.doi.org/10.1149/ma2022-02361334mtgabs.

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In recent years, two-dimensional (2D) materials-based RRAMs have gained high importance because of their thermal and mechanical stability, and better potentiation-depression controllability. 2D materials based conductive bridge random access memory (CBRAM) has been considered as promising approach for neuromorphic and image processing technology [1]. Despite much progress in CMOS technology, the growth and deposition technology of 2D materials for semiconductor integrated circuit are much complex and is generally available at wafer scale [2]. In addition, high growth temperature for high quality of 2D materials complicates direct wafer growth and makes transfer process desirable. At the device level, challenges are linked to controlled and uniform growth of 2D material for high density electronic structure. Recently, discreet 2D based memristor have been used in crossbar structure as synapse for neuromorphic computing. However, the plasma-assisted chemical vapor reaction (PACVR) based memristor for neuromorphic application are rarely demonstrated. Here, we report the co-integration of plasma-assisted chemical vapor reaction (PACVR) with silicon CMOS technology to provide brain-inspired computing device. PACVR offers compatibility with temperature limited 3D integration process and also provides much better thickness control over a large area. Furthermore, it an easy platform for direct and controlled synthesis of TMDs compared to conventional CVD approach. The PACVR grown WSe2 layer (~2 nm) on silicon substrate is realized, which exhibits both threshold and bipolar switching. The threshold and bipolar switching emulate integrate-fire neuron function and is obtained by modulating the compliance current in the device. The dynamics of the switching is closely related to the diffusive dynamics of the active metal (Ag or Cu) which can be controlled by device current. As a result, the WSe2/Si memristor shows synaptic behavior for neuromorphic system with learning accuracy of 96%. References: Wang, C.-Y. et al. 2D layered materials for memristive and neuromorphic applications. Electron. Mater. 6, 1901107 (2020) Zhang, X. et al. Two-dimensional MoS2-enabled flexible rectenna for Wi-Fi-band wireless energy harvesting. Nature 566, 368–372 (2019). Figure 1

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Jin, Chenxing, Wanrong Liu, Yulong Huang, Yunchao Xu, Yiling Nie, Gengming Zhang, Pei He, Jia Sun, and Junliang Yang. "Printable ion-gel-gated In₂O₃ synaptic transistor array for neuro-inspired memory." Applied Physics Letters 120, no. 23 (June 6, 2022): 233701. http://dx.doi.org/10.1063/5.0092968.

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With the development of neuromorphic electronics, much effort has been devoted to the design and manufacture of synaptic electronic devices with large scale and cost-efficient. In this paper, an In2O3 synaptic transistor array gated by screen-printed ion-gel was demonstrated. Due to the ion-gel/Al2O3 stacked gate dielectric, all devices on the array achieved a large hysteresis window of >1 V, a steep back sweep subthreshold swing of <60 mV/decade, and a nonvolatile memory behavior, showing that the screen-printed ion-gel has satisfactory uniformity in large scale. In addition, short-term to long-term plasticity, paired-pulse facilitation, and spike-rate-dependent plasticity are simulated. Based on the plasticity regulated with the spike frequency, a high-pass filter was realized. Flash memory as a special memory model in the nervous system has been simulated in the array. This study provides a unique platform for designing high-performance, repeatable, and stable artificial synapses for the neuromorphic system.

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Mallinson, J. B., S. Shirai, S. K. Acharya, S. K. Bose, E. Galli, and S. A. Brown. "Avalanches and criticality in self-organized nanoscale networks." Science Advances 5, no. 11 (November 2019): eaaw8438. http://dx.doi.org/10.1126/sciadv.aaw8438.

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Current efforts to achieve neuromorphic computation are focused on highly organized architectures, such as integrated circuits and regular arrays of memristors, which lack the complex interconnectivity of the brain and so are unable to exhibit brain-like dynamics. New architectures are required, both to emulate the complexity of the brain and to achieve critical dynamics and consequent maximal computational performance. We show here that electrical signals from self-organized networks of nanoparticles exhibit brain-like spatiotemporal correlations and criticality when fabricated at a percolating phase transition. Specifically, the sizes and durations of avalanches of switching events are power law distributed, and the power law exponents satisfy rigorous criteria for criticality. These signals are therefore qualitatively and quantitatively similar to those measured in the cortex. Our self-organized networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are an important step toward creating neuromorphic device architectures.

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Shahsavari, Mahyar, Jonathan Beaumont, David Thomas, and Andrew D. Brown. "POETS: A Parallel Cluster Architecture for Spiking Neural Network." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 281–85. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1048.

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Spiking Neural Networks (SNNs) are known as a branch of neuromorphic computing and are currently used in neuroscience applications to understand and model the biological brain. SNNs could also potentially be used in many other application domains such as classification, pattern recognition, and autonomous control. This work presents a highly-scalable hardware platform called POETS, and uses it to implement SNN on a very large number of parallel and reconfigurable FPGA-based processors. The current system consists of 48 FPGAs, providing 3072 processing cores and 49152 threads. We use this hardware to implement up to four million neurons with one thousand synapses. Comparison to other similar platforms shows that the current POETS system is twenty times faster than the Brian simulator, and at least two times faster than SpiNNaker.

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Dominguez-Morales, Juan P., D. Gutierrez-Galan, A. Rios-Navarro, L. Duran-Lopez, M. Dominguez-Morales, and A. Jimenez-Fernandez. "pyNAVIS: An open-source cross-platform software for spike-based neuromorphic audio information processing." Neurocomputing 449 (August 2021): 172–75. http://dx.doi.org/10.1016/j.neucom.2021.03.121.

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Cheng, Ran, Khalid B. Mirza, and Konstantin Nikolic. "Neuromorphic Robotic Platform with Visual Input, Processor and Actuator, Based on Spiking Neural Networks." Applied System Innovation 3, no. 2 (June 24, 2020): 28. http://dx.doi.org/10.3390/asi3020028.

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This paper describes the design and modus of operation of a neuromorphic robotic platform based on SpiNNaker, and its implementation on the goalkeeper task. The robotic system utilises an address event representation (AER) type of camera (dynamic vision sensor (DVS)) to capture features of a moving ball, and a servo motor to position the goalkeeper to intercept the incoming ball. At the backbone of the system is a microcontroller (Arduino Due) which facilitates communication and control between different robot parts. A spiking neuronal network (SNN), which is running on SpiNNaker, predicts the location of arrival of the moving ball and decides where to place the goalkeeper. In our setup, the maximum data transmission speed of the closed-loop system is approximately 3000 packets per second for both uplink and downlink, and the robot can intercept balls whose speed is up to 1 m/s starting from the distance of about 0.8 m. The interception accuracy is up to 85%, the response latency is 6.5 ms and the maximum power consumption is 7.15 W. This is better than previous implementations based on PC. Here, a simplified version of an SNN has been developed for the ‘interception of a moving object’ task, for the purpose of demonstrating the platform, however a generalised SNN for this problem is a nontrivial problem. A demo video of the robot goalie is available on YouTube.

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Wu, Chaoxing, Yongai Zhang, Xiongtu Zhou, Dianlun Li, Jae Hyeon Park, Haoqun An, Sihyun Sung, et al. "Binary Electronic Synapses for Integrating Digital and Neuromorphic Computation in a Single Physical Platform." ACS Applied Materials & Interfaces 12, no. 14 (March 16, 2020): 17130–38. http://dx.doi.org/10.1021/acsami.0c02145.

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Wilby, Mark Richard, Ana Belén Rodríguez González, Juan José Vinagre Díaz, and Jesús Requena Carrión. "Neuromorphic Sensor Network Platform: A Bioinspired Tool to Grow Applications in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 11, no. 6 (January 2015): 230401. http://dx.doi.org/10.1155/2015/230401.

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Chen, Guang, Zhenshan Bing, Florian Rohrbein, Jorg Conradt, Kai Huang, Long Cheng, Zhuangyi Jiang, and Alois Knoll. "Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform." IEEE Transactions on Cognitive and Developmental Systems 11, no. 1 (March 2019): 1–12. http://dx.doi.org/10.1109/tcds.2017.2712712.

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D’Abbraccio, Jessica, Luca Massari, Sahana Prasanna, Laura Baldini, Francesca Sorgini, Giuseppe Airò Farulla, Andrea Bulletti, et al. "Haptic Glove and Platform with Gestural Control For Neuromorphic Tactile Sensory Feedback In Medical Telepresence †." Sensors 19, no. 3 (February 3, 2019): 641. http://dx.doi.org/10.3390/s19030641.

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Advancements in the study of the human sense of touch are fueling the field of haptics. This is paving the way for augmenting sensory perception during object palpation in tele-surgery and reproducing the sensed information through tactile feedback. Here, we present a novel tele-palpation apparatus that enables the user to detect nodules with various distinct stiffness buried in an ad-hoc polymeric phantom. The contact force measured by the platform was encoded using a neuromorphic model and reproduced on the index fingertip of a remote user through a haptic glove embedding a piezoelectric disk. We assessed the effectiveness of this feedback in allowing nodule identification under two experimental conditions of real-time telepresence: In Line of Sight (ILS), where the platform was placed in the visible range of a user; and the more demanding Not In Line of Sight (NILS), with the platform and the user being 50 km apart. We found that the entailed percentage of identification was higher for stiffer inclusions with respect to the softer ones (average of 74% within the duration of the task), in both telepresence conditions evaluated. These promising results call for further exploration of tactile augmentation technology for telepresence in medical interventions.

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Chen, Li, Mei Er Pam, Sifan Li, and Kah-Wee Ang. "Ferroelectric memory based on two-dimensional materials for neuromorphic computing." Neuromorphic Computing and Engineering 2, no. 2 (March 25, 2022): 022001. http://dx.doi.org/10.1088/2634-4386/ac57cb.

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Abstract Ferroelectric memory devices with fast-switching speed and ultra-low power consumption have been recognized as promising building blocks for brain-like neuromorphic computing. In particular, ferroelectric memories based on 2D materials are attracting increasing research interest in recent years due to their unique properties that are unattainable in conventional materials. Specifically, the atomically thin 2D materials with tunable electronic properties coupled with the high compatibility with existing complementary metal-oxide-semiconductor technology manifests their potential for extending state-of-the-art ferroelectric memory technology into atomic-thin scale. Besides, the discovery of 2D materials with ferroelectricity shows the potential to realize functional devices with novel structures. This review will highlight the recent progress in ferroelectric memory devices based on 2D materials for neuromorphic computing. The merits of such devices and the range of 2D ferroelectrics being explored to date are reviewed and discussed, which include two- and three-terminal ferroelectric synaptic devices based on 2D materials platform. Finally, current developments and remaining challenges in achieving high-performance 2D ferroelectric synapses are discussed.

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Goteti, Uday S., Ivan A. Zaluzhnyy, Shriram Ramanathan, Robert C. Dynes, and Alex Frano. "Low-temperature emergent neuromorphic networks with correlated oxide devices." Proceedings of the National Academy of Sciences 118, no. 35 (August 25, 2021): e2103934118. http://dx.doi.org/10.1073/pnas.2103934118.

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Neuromorphic computing—which aims to mimic the collective and emergent behavior of the brain’s neurons, synapses, axons, and dendrites—offers an intriguing, potentially disruptive solution to society’s ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.

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Devyatisil’nyi, A. S. "Inertial satellite neuromorphic system for estimation of the rotation parameters of a mobile technological platform." Technical Physics 59, no. 10 (October 2014): 1424–27. http://dx.doi.org/10.1134/s1063784214100120.

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Adda, C., H. Navarro, J. Kaur, M. H. Lee, C. Chen, M. Rozenberg, S. P. Ong, and Ivan K. Schuller. "An optoelectronic heterostructure for neuromorphic computing: CdS/V₃O₅." Applied Physics Letters 121, no. 4 (July 25, 2022): 041901. http://dx.doi.org/10.1063/5.0103650.

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Nonvolatile resistive switching is one of the key phenomena for emerging applications in optoelectronics and neuromorphic computing. In most of the cases, an electric field is applied to a two terminal dielectric material device and leads to the formation of a low resistance filament due to ion migration. However, the stochastic nature of the ion migration can be an impediment for the device robustness and controllability, with uncontrolled variations of high and low resistance states or threshold voltages. Here, we report an optically induced resistive switching based on a CdS/V3O5 heterostructure which can overcome this issue. V3O5 is known to have a second order insulator to metal transition around Tc ≈ 415 K, with an electrically induced threshold switching at room temperature. Upon illumination, the direct transfer of the photoinduced carriers from the CdS into V3O5 produces a nonvolatile resistive switching at room temperature. The initial high resistance can be recovered by reaching the high temperature metallic phase, i.e., temperatures above Tc. Interestingly, this resistive switching becomes volatile around the Tc. By locally manipulating the volatile and nonvolatile resistive switching using electric field and light, this system is a promising platform for hardware based neuromorphic computing implementations.

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Devyatisilnyi, A. S., and K. A. Chislov. "Neuromorphic estimation of motion parameters for geodesic platform with non-nuclear tuning mechanism of synaptic coefficients." Geodesy and Cartography 904, no. 10 (November 20, 2015): 8–12. http://dx.doi.org/10.22389/0016-7126-2015-904-10-8-12.

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Sugiarto, Indar, and Steve Furber. "Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 1 (February 1, 2021): 182. http://dx.doi.org/10.12928/telkomnika.v19i1.15026.

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Yi, Ailun, Chengli Wang, Liping Zhou, Yifan Zhu, Shibin Zhang, Tiangui You, Jiaxiang Zhang, and Xin Ou. "Silicon carbide for integrated photonics." Applied Physics Reviews 9, no. 3 (September 2022): 031302. http://dx.doi.org/10.1063/5.0079649.

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Photonic integrated circuits (PICs) based on lithographically patterned waveguides provide a scalable approach for manipulating photonic bits, enabling seminal demonstrations of a wide range of photonic technologies with desired complexity and stability. While the next generation of applications such as ultra-high speed optical transceivers, neuromorphic computing and terabit-scale communications demand further lower power consumption and higher operating frequency. Complementing the leading silicon-based material platforms, the third-generation semiconductor, silicon carbide (SiC), offers a significant opportunity toward the advanced development of PICs in terms of its broadest range of functionalities, including wide bandgap, high optical nonlinearities, high refractive index, controllable artificial spin defects and complementary metal oxide semiconductor-compatible fabrication process. The superior properties of SiC have enabled a plethora of nano-photonic explorations, such as waveguides, micro-cavities, nonlinear frequency converters and optically-active spin defects. This remarkable progress has prompted the rapid development of advanced SiC PICs for both classical and quantum applications. Here, we provide an overview of SiC-based integrated photonics, presenting the latest progress on investigating its basic optoelectronic properties, as well as the recent developments in the fabrication of several typical approaches for light confinement structures that form the basic building blocks for low-loss, multi-functional and industry-compatible integrated photonic platform. Moreover, recent works employing SiC as optically-readable spin hosts for quantum information applications are also summarized and highlighted. As a still-developing integrated photonic platform, prospects and challenges of utilizing SiC material platforms in the field of integrated photonics are also discussed.

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Patiño-Saucedo, Alberto, Horacio Rostro-Gonzalez, Teresa Serrano-Gotarredona, and Bernabé Linares-Barranco. "Event-driven implementation of deep spiking convolutional neural networks for supervised classification using the SpiNNaker neuromorphic platform." Neural Networks 121 (January 2020): 319–28. http://dx.doi.org/10.1016/j.neunet.2019.09.008.

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Montano, Kevin, Gianluca Milano, and Carlo Ricciardi. "Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks." Neuromorphic Computing and Engineering 2, no. 1 (February 11, 2022): 014007. http://dx.doi.org/10.1088/2634-4386/ac4d86.

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Abstract Self-assembled memristive nanonetworks composed of many interacting nano objects have been recently exploited for neuromorphic-type data processing and for the implementation of unconventional computing paradigms, such as reservoir computing. In these networks, information processing and computing tasks are performed by exploiting the emergent network behaviour without the need of fine tuning its components. Here, we propose grid-graph modelling of the emergent behaviour of memristive nanonetworks, where the memristive behaviour is decoupled from the particular and detailed behaviour of each network element. In this model, the memristive behavior of each edge is regulated by an analytical potentiation-depression rate balance equation deduced from physical arguments. By comparing modelling and experimental results obtained on nanonetworks based on Ag NWs, the model is shown to be able to emulate the main features of the emergent memristive behaviour and spatio-temporal dynamics of the nanonetwork, including short-term plasticity, paired-pulse facilitation and heterosynaptic plasticity. These results show that the model represents a versatile platform for exploring the implementation of unconventional computing paradigms in nanonetworks.

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Vanarse, Anup, Adam Osseiran, Alexander Rassau, and Peter van der Made. "A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data." Sensors 19, no. 22 (November 6, 2019): 4831. http://dx.doi.org/10.3390/s19224831.

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In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier.

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Monalisha, P., Shengyao Li, Shwetha G. Bhat, Tianli Jin, P. S. Anil Kumar, and S. N. Piramanayagam. "Synaptic behavior of Fe₃O₄-based artificial synapse by electrolyte gating for neuromorphic computing." Journal of Applied Physics 133, no. 8 (February 28, 2023): 084901. http://dx.doi.org/10.1063/5.0120854.

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Neuromorphic computing (NC) is a crucial step toward realizing power-efficient artificial intelligence systems. Hardware implementation of NC is expected to overcome the challenges associated with the conventional von Neumann computer architecture. Synaptic devices that can emulate the rich functionalities of biological synapses are emerging. Out of several approaches, electrolyte-gated synaptic transistors have attracted enormous scientific interest owing to their similar working mechanism. Here, we report a three-terminal electrolyte-gated synaptic transistor based on Fe3O4 thin films, a half-metallic spinel ferrite. We have realized gate-controllable multilevel, non-volatile, and rewritable states for analog computing. Furthermore, we have emulated essential synaptic functions by applying electrical stimulus to the gate terminal of the synaptic device. This work provides a new candidate and a platform for spinel ferrite-based devices for future NC applications.

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Min, Jin-Gi, Hamin Park, and Won-Ju Cho. "Milk–Ta2O5 Hybrid Memristors with Crossbar Array Structure for Bio-Organic Neuromorphic Chip Applications." Nanomaterials 12, no. 17 (August 28, 2022): 2978. http://dx.doi.org/10.3390/nano12172978.

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In this study, a high-performance bio-organic memristor with a crossbar array structure using milk as a resistive switching layer (RSL) is proposed. To ensure compatibility with the complementary metal oxide semiconductor process of milk RSL, a high-k Ta2O5 layer was deposited as a capping layer; this layer enables high-density, integration-capable, photolithography processes. The fabricated crossbar array memristors contain milk–Ta2O5 hybrid membranes, and they exhibit bipolar resistance switching behavior and uniform resistance distribution across hundreds of repeated test cycles. In terms of the artificial synaptic behavior and synaptic weight changes, milk–Ta2O5 hybrid crossbar array memristors have a stable analog RESET process, and the memristors are highly responsive to presynaptic stimulation via paired-pulse facilitation excitatory post-synaptic current. Moreover, spike-timing-dependent plasticity and potentiation and depression behaviors, which closely emulate long-term plasticity and modulate synaptic weights, were evaluated. Finally, an artificial neural network was designed and trained to recognize the pattern of the Modified National Institute of Standards and Technology (MNIST) digits to evaluate the capability of the neuromorphic computing system. Consequently, a high recognition rate of over 88% was achieved. Thus, the milk–Ta2O5 hybrid crossbar array memristor is a promising electronic platform for in-memory computing systems.

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Ma, Mangyuan, Ke Huang, Yong Li, Sihua Li, Qiyuan Feng, Calvin Ching Ian Ang, Tianli Jin, et al. "Nano-engineering the evolution of skyrmion crystal in synthetic antiferromagnets." Applied Physics Reviews 9, no. 2 (June 2022): 021404. http://dx.doi.org/10.1063/5.0081455.

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The evolution of skyrmion crystals encapsulates skyrmion's critical behaviors, such as nucleation, deformation, and annihilation. Here, we achieve a tunable evolution of artificial skyrmion crystals in nanostructured synthetic antiferromagnet multilayers, which are composed of perpendicular magnetic multilayers and nanopatterned arrays of magnetic nanodots. The out-of-plane magnetization hysteresis loops and first-order reversal curves show that the nucleation and annihilation of the artificial skyrmion can be controlled by tuning the diameter of and spacing between the nanodots. Moreover, when the bottom layer thickness increases, the annihilation of skyrmion shifts from evolving into a ferromagnetic spin texture to evolving into an antiferromagnetic spin texture. Most significantly, nonvolatile multiple states are realized at zero magnetic field via controlling the proportion of the annihilated skyrmions in the skyrmion crystal. Our results demonstrate the tunability and flexibility of the artificial skyrmion platform, providing a promising route to achieve skyrmion-based multistate devices, such as neuromorphic spintronic devices.

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Pradhan, Basudev, Sonali Das, Jinxin Li, Farzana Chowdhury, Jayesh Cherusseri, Deepak Pandey, Durjoy Dev, et al. "Ultrasensitive and ultrathin phototransistors and photonic synapses using perovskite quantum dots grown from graphene lattice." Science Advances 6, no. 7 (February 2020): eaay5225. http://dx.doi.org/10.1126/sciadv.aay5225.

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Organic-inorganic halide perovskite quantum dots (PQDs) constitute an attractive class of materials for many optoelectronic applications. However, their charge transport properties are inferior to materials like graphene. On the other hand, the charge generation efficiency of graphene is too low to be used in many optoelectronic applications. Here, we demonstrate the development of ultrathin phototransistors and photonic synapses using a graphene-PQD (G-PQD) superstructure prepared by growing PQDs directly from a graphene lattice. We show that the G-PQDs superstructure synchronizes efficient charge generation and transport on a single platform. G-PQD phototransistors exhibit excellent responsivity of 1.4 × 108 AW–1 and specific detectivity of 4.72 × 1015 Jones at 430 nm. Moreover, the light-assisted memory effect of these superstructures enables photonic synaptic behavior, where neuromorphic computing is demonstrated by facial recognition with the assistance of machine learning. We anticipate that the G-PQD superstructures will bolster new directions in the development of highly efficient optoelectronic devices.

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Hughes, Mark A., Mike J. Shipston, and Alan F. Murray. "Towards a ‘siliconeural computer’: technological successes and challenges." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2046 (July 28, 2015): 20140217. http://dx.doi.org/10.1098/rsta.2014.0217.

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Electronic signals govern the function of both nervous systems and computers, albeit in different ways. As such, hybridizing both systems to create an iono-electric brain–computer interface is a realistic goal; and one that promises exciting advances in both heterotic computing and neuroprosthetics capable of circumventing devastating neuropathology. ‘Neural networks’ were, in the 1980s, viewed naively as a potential panacea for all computational problems that did not fit well with conventional computing. The field bifurcated during the 1990s into a highly successful and much more realistic machine learning community and an equally pragmatic, biologically oriented ‘neuromorphic computing’ community. Algorithms found in nature that use the non-synchronous, spiking nature of neuronal signals have been found to be (i) implementable efficiently in silicon and (ii) computationally useful. As a result, interest has grown in techniques that could create mixed ‘siliconeural’ computers. Here, we discuss potential approaches and focus on one particular platform using parylene-patterned silicon dioxide.

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Yanushkevich, Svetlana, Hong Tran, Golam Tangim, Vladimir Shmerko, Elena Zaitseva, and Vitaly Levashenko. "The EXOR gate under uncertainty: A case study." Facta universitatis - series: Electronics and Energetics 24, no. 3 (2011): 451–82. http://dx.doi.org/10.2298/fuee1103451y.

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Probabilistic AND/EXOR networks have been defined, in the past, as a class of Reed-Muller circuits, which operate on random signals. In contemporary logic network design, it is classified as behavioral notation of probabilistic logic gates and networks. In this paper, we introduce additional notations of probabilistic AND/EXOR networks: belief propagation, stochastic, decision diagram, neuromorphic models, and Markov random field model. Probabilistic logic networks, and, in particular, probabilistic AND/EXOR networks, known as turbo-decoders (used in cell phones and iPhone) are in demand in the coding theory. Another example is intelligent decision support in banking and security applications. We argue that there are two types of probabilistic networks: traditional logic networks assuming random signals, and belief propagation networks. We propose the taxonomy for this design, and provide the results of experimental study. In addition, we show that in forthcoming technologies, in particular, molecular electronics, probabilistic computing is the platform for developing the devices and systems for low-power low-precise data processing.

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Lee, Wang Wei, Yu Jun Tan, Haicheng Yao, Si Li, Hian Hian See, Matthew Hon, Kian Ann Ng, Betty Xiong, John S. Ho, and Benjamin C. K. Tee. "A neuro-inspired artificial peripheral nervous system for scalable electronic skins." Science Robotics 4, no. 32 (July 17, 2019): eaax2198. http://dx.doi.org/10.1126/scirobotics.aax2198.

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The human sense of touch is essential for dexterous tool usage, spatial awareness, and social communication. Equipping intelligent human-like androids and prosthetics with electronic skins—a large array of sensors spatially distributed and capable of rapid somatosensory perception—will enable them to work collaboratively and naturally with humans to manipulate objects in unstructured living environments. Previously reported tactile-sensitive electronic skins largely transmit the tactile information from sensors serially, resulting in readout latency bottlenecks and complex wiring as the number of sensors increases. Here, we introduce the Asynchronously Coded Electronic Skin (ACES)—a neuromimetic architecture that enables simultaneous transmission of thermotactile information while maintaining exceptionally low readout latencies, even with array sizes beyond 10,000 sensors. We demonstrate prototype arrays of up to 240 artificial mechanoreceptors that transmitted events asynchronously at a constant latency of 1 ms while maintaining an ultra-high temporal precision of <60 ns, thus resolving fine spatiotemporal features necessary for rapid tactile perception. Our platform requires only a single electrical conductor for signal propagation, realizing sensor arrays that are dynamically reconfigurable and robust to damage. We anticipate that the ACES platform can be integrated with a wide range of skin-like sensors for artificial intelligence (AI)–enhanced autonomous robots, neuroprosthetics, and neuromorphic computing hardware for dexterous object manipulation and somatosensory perception.

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Journal articles on the topic 'Neuromorphic platform'

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