Academic literature on the topic 'Neuromorphic technologies/devices'
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Journal articles on the topic "Neuromorphic technologies/devices":
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Orii, Yasumitsu, Akihiro Horibe, Kuniaki Sueoka, Keiji Matsumoto, Toyohiro Aoki, Hirokazu Noma, Sayuri Kohara, et al. "PERSPECTIVE ON REQUIRED PACKAGING TECHNOLOGIES FOR NEUROMORPHIC DEVICES." International Symposium on Microelectronics 2015, no. 1 (October 1, 2015): 000561–66. http://dx.doi.org/10.4071/isom-2015-tha15.
Abstract:
Cognitive computing has capability of machine learning, recognition and proposal. It is essential to make human life richer, more productive and more intelligent. For the realization of the cognitive computing, an efficient and scalable non-von Neumann architecture inspired by the human brain structure has been developed and a device which demonstrates the concept was also built. This device mimics the signal processing of the human brain, packing one million neuron circuits in 4,096 cores. It consumes almost 1,000 times less energy per event compared with a state-of-the-art multiprocessor. However, one million neurons only correspond to those of the bee's brain, and to mimic the brains of higher order animals, the inter-chip wiring becomes much more important, because this kind of neuromorphic device requires a large number of parallel signal lines for massive parallel signal operations. 3D chip stacking is, of course, a crucial technology in achieving the device. Technologies associated with 3D stacking such as low cost TSV formation and fine-pitch interconnection, smaller than 10μm pitch technology are required. From the reliability point of view, the optimization of solder composition is also important. Injection Molded Solder (IMS) is well fit to this fine pitch interconnection, in terms of material optimization and low cost joints. As for the interposer, the build-up organic interposer is the most attractive candidates for the cost issue, but in the most top layer, ultra-fine pitch wiring with the line and space widths smaller than 1μm should be prepared. Lots of material and process innovations are necessary for the inter-chip connection for neuromorphic devices.
2
Diao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (December 12, 2023): 9779. http://dx.doi.org/10.3390/s23249779.
Abstract:
As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.
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Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive and CMOS Devices for Neuromorphic Computing." Materials 13, no. 1 (January 1, 2020): 166. http://dx.doi.org/10.3390/ma13010166.
Abstract:
Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.
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Abbas, Haider, Jiayi Li, and Diing Shenp Ang. "Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications." Micromachines 13, no. 5 (April 30, 2022): 725. http://dx.doi.org/10.3390/mi13050725.
Abstract:
Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.
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Allwood, Dan A., Matthew O. A. Ellis, David Griffin, Thomas J. Hayward, Luca Manneschi, Mohammad F. KH Musameh, Simon O'Keefe, et al. "A perspective on physical reservoir computing with nanomagnetic devices." Applied Physics Letters 122, no. 4 (January 23, 2023): 040501. http://dx.doi.org/10.1063/5.0119040.
Abstract:
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here, we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.
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Della Rocca, Mattia. "Of the Artistic Nude and Technological Behaviorism." Nuncius 32, no. 2 (2017): 376–411. http://dx.doi.org/10.1163/18253911-03202006.
Abstract:
Neuromorphic technologies lie at the core of 21st century neuroscience, especially in the “big brain science” projects started in 2013 – i.e. the BRAIN Initiative and the Human Brain Project. While neuromorphism and the “reverse engineering” of the brain are often presented as a “methodological revolution” in the brain sciences, these concepts have a long history which is strongly interconnected with the developments in neuroscience and the related field of bioengineering since the end of World War II. In this paper I provide a short review of the first generation of “neuromorphic devices” created in the 1960s, by focusing on the work of Leon Harmon and his “neuromime,” whose material history overlapped in a very interesting sense with the visual and artistic culture of the second half of the 20th century.
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Kurshan, Eren, Hai Li, Mingoo Seok, and Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications." International Journal of Semantic Computing 14, no. 04 (December 2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.
Abstract:
Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefit the designs in the future.
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Hajtó, Dániel, Ádám Rák, and György Cserey. "Robust Memristor Networks for Neuromorphic Computation Applications." Materials 12, no. 21 (October 31, 2019): 3573. http://dx.doi.org/10.3390/ma12213573.
Abstract:
One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.
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Covi, Erika, Halid Mulaosmanovic, Benjamin Max, Stefan Slesazeck, and Thomas Mikolajick. "Ferroelectric-based synapses and neurons for neuromorphic computing." Neuromorphic Computing and Engineering 2, no. 1 (February 7, 2022): 012002. http://dx.doi.org/10.1088/2634-4386/ac4918.
Abstract:
Abstract The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as spiking neural networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems’ power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technologies for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.
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Sueoka, Brandon, and Feng Zhao. "Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems." Journal of Physics D: Applied Physics 55, no. 22 (March 7, 2022): 225105. http://dx.doi.org/10.1088/1361-6463/ac585b.
Abstract:
Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems.
Dissertations / Theses on the topic "Neuromorphic technologies/devices":
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Calayir, Vehbi. "Neurocomputing and Associative Memories Based on Emerging Technologies: Co-optimization of Technology and Architecture." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/422.
Abstract:
Neurocomputers offer a massively parallel computing paradigm by mimicking the human brain. Their efficient use in statistical information processing has been proposed to overcome critical bottlenecks with traditional computing schemes for applications such as image and speech processing, and associative memory. In neural networks information is generally represented by phase (e.g., oscillatory neural networks) or amplitude (e.g., cellular neural networks). Phase-based neurocomputing is constructed as a network of coupled oscillatory neurons that are connected via programmable phase elements. Representing each neuron circuit with one oscillatory device and implementing programmable phases among neighboring neurons, however, are not clearly feasible from circuits perspective if not impossible. In contrast to nascent oscillatory neurocomputing circuits, mature amplitude-based neural networks offer more efficient circuit solutions using simpler resistive networks where information is carried via voltage- and current-mode signals. Yet, such circuits have not been efficiently realized by CMOS alone due to the needs for an efficient summing mechanism for weighted neural signals and a digitally-controlled weighting element for representing couplings among artificial neurons. Large power consumption and high circuit complexity of such CMOS-based implementations have precluded adoption of amplitude-based neurocomputing circuits as well, and have led researchers to explore the use of emerging technologies for such circuits. Although they provide intriguing properties, previously proposed neurocomputing components based on emerging technologies have not offered a complete and practical solution to efficiently construct an entire system. In this thesis we explore the generalized problem of co-optimization of technology and architecture for such systems, and develop a recipe for device requirements and target capabilities. We describe four plausible technologies, each of which could potentially enable the implementation of an efficient and fully-functional neurocomputing system. We first investigate fully-digital neural network architectures that have been tried before using CMOS technology in which many large-size logic gates such as D flip-flops and look-up tables are required. Using a newly-proposed all-magnetic non-volatile logic family, mLogic, we demonstrate the efficacy of digitizing the oscillators and phase relationships for an oscillatory neural network by exploiting the inherent storage as well as enabling an all-digital cellular neural network hardware with simplified programmability. We perform system-level comparisons of mLogic and 32nm CMOS for both networks consisting of 60 neurons. Although digital implementations based on mLogic offer improvements over CMOS in terms of power and area, analog neurocomputing architectures seem to be more compatible with the greatest portion of emerging technologies and devices. For this purpose in this dissertation we explore several emerging technologies with unique device configurations and features such as mCell devices, ovenized aluminum nitride resonators, and tunable multi-gate graphene devices to efficiently enable two key components required for such analog networks – that is, summing function and weighting with compact D/A (digital-to-analog) conversion capability. We demonstrate novel ways to implement these functions and elaborate on our building blocks for artificial neurons and synapses using each technology. We verify the functionality of each proposed implementation using various image processing applications based on compact circuit simulation models for such post-CMOS devices. Finally, we design a proof-of-concept neurocomputing circuitry containing 20 neurons using 65nm CMOS technology that is based on the primitives that we define for our analog neurocomputing scheme. This allows us to fully recognize the inefficiencies of an all-CMOS implementation for such specific applications. We share our experimental results that are in agreement with circuit simulations for the same image processing applications based on proposed architectures using emerging technologies. Power and area comparisons demonstrate significant improvements for analog neurocomputing circuits when implemented using beyond- CMOS technologies, thereby promising huge opportunities for future energy-efficient computing.
2
Janzakova, Kamila. "Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILN017.
Abstract:
Les technologies neuromorphiques constituent une voie prometteuse pour le développement d'une informatique plus avancée et plus économe en énergie. Elles visent à reproduire les caractéristiques attrayantes du cerveau, telles qu'une grande efficacité de calcul et une faible consommation d'énergie au niveau des logiciels et du matériel. À l'heure actuelle, les implémentations logicielles inspirées du cerveau (telles que ANN et SNN) ont déjà démontré leur efficacité dans différents types de tâches (reconnaissance d'images et de la parole). Toutefois, pour tirer un meilleur parti des algorithmes inspirés du cerveau, il est possible de les combiner avec une implémentation materielle appropriée qui s'appuierait également sur une architecture et des processus inspirés du cerveau. L'ingénierie neuromorphique s'est principalement appuyée sur les technologies conventionnelles (CMOS circuits, memristor) pour le développement de circuits inspirés du cerveau. Néanmoins, ces implémentations sont fabriquées suivant une approche top-down. En revanche, l'informatique cérébrale repose sur des processus bottom-up tels que l'interconnectivité entre les cellules et la formation de voies de communication neuronales.À la lumière de ce qui précède, ce travail de thèse porte sur le développement de dispositifs neuromorphiques organiques programmables en 3D qui, contrairement à la plupart des technologies neuromorphiques actuelles, peuvent être créés de manière bottom-up. Cela permet de rapprocher les technologies neuromorphiques du niveau de programmation du cerveau, où les chemins neuronaux nécessaires sont établis uniquement en fonction des besoins.Tout d'abord, nous avons découvert que les interconnexions 3D à base de PEDOT:PSS peuvent être formées au moyen d'électropolymérisation bipolaire en courant alternatif, permettant d'imiter la croissance des cellules neuronales. En réglant individuellement les paramètres de la forme d'onde (tension d'amplitude de crête - VP, fréquence - f, duty cycle- dc et tension de décalage - Voff), une large gamme de structures semblables à des dendrites a été observée avec différents degrés de ramification, volumes, surfaces, asymétries et dynamiques de croissance.Ensuite, nous avons montré que les morphologies dendritiques obtenues à différentes fréquences sont conductrices. De plus, chaque structure présente une valeur de conductance qui peut être interprétée comme un poids synaptique. Plus important encore, la capacité des dendrites à fonctionner comme OECT a été révélée. Différentes morphologies de dendrites ont présenté des performances différentes en tant qu'OECT. De plus, la capacité des dendrites en PEDOT:PSS à modifier leur conductivité en réponse à la tension de grille a été utilisée pour imiter les fonctions de mémoire du cerveau (plasticité à court terme -STP et plasticité à long terme -LTP). Les réponses à la STP varient en fonction de la structure dendritique. En outre, l'émulation de la LTP a été démontrée non seulement au moyen d'un fil de grille Ag/AgCl, mais aussi au moyen d'une grille dendritique en polymère développée par électropolymérisation.Enfin, la plasticité structurelle a été démontrée par la croissance dendritique, où le poids de la connexion finale est régi par les règles d'apprentissage de type Hebbien (plasticité dépendante du moment de l'impulsion - STDP et plasticité dépendante du rythme de l'impulsion - SRDP). En utilisant les deux approches, une variété de topologies dendritiques avec des états de conductance programmables (c'est-à-dire le poids synaptique) et diverses dynamiques de croissance ont été observées. Finalement, en utilisant la même plasticité structurelle dendritique, des caractéristiques cérébrales plus complexes telles que l'apprentissage associatif et les tâches de classification ont été émulées.En outre, les perspectives futures de ces technologies basées sur des objets dendritiques polymères ont été discutéesNeuromorphic technologies is a promising direction for development of more advanced and energy-efficient computing. They aim to replicate attractive brain features such as high computational efficiency at low power consumption on a software and hardware level. At the moment, brain-inspired software implementations (such as ANN and SNN) have already shown their successful application for different types of tasks (image and speech recognition). However, to benefit more from the brain-like algorithms, one may combine them with appropriate hardware that would also rely on brain-like architecture and processes and thus complement them. Neuromorphic engineering has already shown the utilization of solid-state electronics (CMOS circuits, memristor) for the development of brain-inspired devices. Nevertheless, these implementations are fabricated through top-down methods. In contrast, brain computing relies on bottom-up processes such as interconnectivity between cells and the formation of neural communication pathways.In the light of mentioned above, this work reports on the development of programmable 3D organic neuromorphic devices, which, unlike most current neuromorphic technologies, can be created in a bottom-up manner. This allows bringing neuromorphic technologies closer to the level of brain programming, where necessary neural paths are established only on the need.First, we found out that PEDOT:PSS based 3D interconnections can be formed by means of AC-bipolar electropolymerization and that they are capable of mimicking the growth of neural cells. By tuning individually the parameters of the waveform (peak amplitude voltage -VP, frequency - f, duty cycle - dc and offset voltage - Voff), a wide range of dendrite-like structures was observed with various branching degrees, volumes, surface areas, asymmetry of formation, and even growth dynamics.Next, it was discovered that dendritic morphologies obtained at various frequencies are conductive. Moreover, each structure exhibits an individual conductance value that can be interpreted as synaptic weight. More importantly, the ability of dendrites to function as OECT was revealed. Different dendrites exhibited different performances as OECT. Further, the ability of PEDOT:PSS dendrites to change their conductivity in response to gate voltage was used to mimic brain memory functions (short-term plasticity -STP and long-term plasticity -LTP). STP responses varied depending on the dendritic structure. Moreover, emulation of LTP was demonstrated not only by means of an Ag/AgCl gate wire but as well by means of a self-developed polymer dendritic gate.Finally, structural plasticity was demonstrated through dendritic growth, where the weight of the final connection is governed according to Hebbian learning rules (spike-timing-dependent plasticity - STDP and spike-rate-dependent plasticity - SRDP). Using both approaches, a variety of dendritic topologies with programmable conductance states (i.e., synaptic weight) and various dynamics of growth have been observed. Eventually, using the same dendritic structural plasticity, more complex brain features such as associative learning and classification tasks were emulated.Additionally, future perspectives of such technologies based on self-propagating polymer dendritic objects were discussed
Book chapters on the topic "Neuromorphic technologies/devices":
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Ricci, Saverio, Piergiulio Mannocci, Matteo Farronato, Alessandro Milozzi, and Daniele Ielmini. "Development of Crosspoint Memory Arrays for Neuromorphic Computing." In Special Topics in Information Technology, 65–74. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51500-2_6.
Abstract:
AbstractMemristor-based hardware accelerators play a crucial role in achieving energy-efficient big data processing and artificial intelligence, overcoming the limitations of traditional von Neumann architectures. Resistive-switching memories (RRAMs) combine a simple two-terminal structure with the possibility of tuning the device conductance. This Chapter revolves around the topic of emerging memristor-related technologies, starting from their fabrication, through the characterization of single devices up to the development of proof-of-concept experiments in the field of in-memory computing, hardware accelerators, and brain-inspired architecture. Non-volatile devices are optimized for large-size crossbars where the devices’ conductance encodes mathematical coefficients of matrices. By exploiting Kirchhoff’s and Ohm’s law the matrix–vector-multiplication between the conductance matrix and a voltage vector is computed in one step. Eigenvalues/eigenvectors are experimentally calculated according to the power-iteration algorithm, with a fast convergence within about 10 iterations to the correct solution and Principal Component Analysis of the Wine and Iris datasets, showing up to 98% accuracy comparable to a floating-point implementation. Volatile memories instead present a spontaneous change of device conductance with a unique similarity to biological neuron behavior. This characteristic is exploited to demonstrate a simple fully-memristive architecture of five volatile RRAMs able to learn, store, and distinguish up to 10 different items with a memory capability of a few seconds. The architecture is thus tested in terms of robustness under many experimental conditions and it is compared with the real brain, disclosing interesting mechanisms which resemble the biological brain.
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Carstens, Niko, Maik-Ivo Terasa, Pia Holtz, Sören Kaps, Thomas Strunskus, Abdou Hassanien, Rainer Adelung, Franz Faupel, and Alexander Vahl. "Memristive Switching: From Individual Nanoparticles Towards Complex Nanoparticle Networks." In Springer Series on Bio- and Neurosystems, 219–39. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36705-2_9.
Abstract:
AbstractNovel hardware concepts in the framework of neuromorphic engineering are intended to overcome fundamental limits of current computer technologies and to be capable of efficient mass data processing. To reach this, research into material systems which enable the implementation of memristive switching in electronic devices, as well as into analytical approaches helping to understand fundamental mechanisms and dynamics of memristive switching is inevitable. In this chapter, memristive switching based on Ag metal filament formation is discussed throughout different scales, providing insights on the stability of metal filaments and the onset of collective behaviour. An unconventional cAFM approach, which intends to integrate the memristive system directly on the apex of the cantilever instead of usual contacting is presented. This facilitates the nanoscale probing of filamentary memristive switching dynamics on long time scales for the purpose of basic research, which is demonstrated by an archetypical electrochemical metallization (ECM) based system consisting of Ag/Si3N4/Au. Further, the application of AgAu and AgPt noble metal alloy nanoparticles (NPs) for memristive devices is discussed with special focus on the device scalability. For the smallest scale it is shown, that a single AgPt-NP encapsulated in SiO2 operates via stable diffusive switching. Finally, two concepts for the self-assembled fabrication of NP-based memristive switch networks are evaluated regarding to collective switching dynamics: A sub-percolated CNT network decorated with AgAu-NPs and a Ag-NP network poised at the percolation threshold. The hybrid CNT/AgAu-NPs networks exhibit a mixed form of diffusive and bipolar switching, which is very interesting for tailoring the retention time, while the networks dynamics of percolated Ag-NP networks are governed by ongoing transitions between a multitude of metastable states, which makes them interesting for reservoir computing and other neuromorphic computation schemes.
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Walters, B., C. Lammie, J. Eshraghian, C. Yakopcic, T. Taha, R. Genov, M. V. Jacob, A. Amirsoleimani, and M. R. Azghadi. "Memristive Devices for Neuromorphic and Deep Learning Applications." In Advanced Memory Technology, 680–704. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00680.
Abstract:
Neuromorphic and deep learning (DL) algorithms are important research areas gaining significant traction of late. Due to this growing interest and the high demand for low-power and high-performance designs for running these algorithms, various circuits and devices are being designed and investigated to realize efficient neuromorphic and DL architectures. One device said to drastically improve this architecture is the memristor. In this chapter, studies investigating memristive implementations into neuromorphic and DL designs are summarized and categorized based on the switching mechanicsms of a few prominent memristive device technologies. Furthermore, the simulation platforms used to model both neuromorphic and DL hardware implementations, which use memristors, are summarized and discussed. This chapter can provide a quick reference for readers interested in learning the latest advancements in the areas of memristive devices and systems for use in neuromorphic and DL systems.
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Shanbogh, Shobith M., R. Anju Kumari, and Ponnam Anjaneyulu. "Hybrid Devices for Neuromorphic Applications." In Advanced Memory Technology, 622–55. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00622.
Abstract:
The world always seeks new materials, devices and technologies for a better future, and thus researchers keep exploring the possibilities. Advanced memory technology also aims to make the world better, comfortable, accessible and explorable. In this direction, hybrid devices consisting of dissimilar materials stacked or fused together can be considered as propitious. An attempt is made to identify the advantages of hybrid structures by implementing them into new memory technology architectures. Hybrid device structures including organic–inorganic, inorganic–inorganic (with different dimensions), an inorganic composite stacked between polymers, organic–perovskite, organic–organic and organic–biomolecule structures are discussed to showcase various memory related applications. The applications include digital memory, analog memory, multibit memory, and synapses. The neuromorphic application of these devices is also mentioned wherever possible. Some concepts like digital and analog memory, multibit memory and synapses are discussed elaborately. A crisp and easy way of understanding the neuromorphic application is presented in a schematic way for the comfort of the reader.
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Ahmed, T., V. Krishnamurthi, and S. Walia. "Working Dynamics in Low-dimensional Material-based Neuromorphic Devices." In Advanced Memory Technology, 458–97. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00458.
Abstract:
The exotic properties of low-dimensional materials have enabled brain-inspired computation to be unprecedently achieved in a variety of electronic and optoelectronic devices. With a plethora of highly efficient memory devices and architectures being developed lately for neuromorphic engineering and technology, the question of what types of materials and physical mechanisms will be used in futuristic neuromorphic devices is still open-ended. For this reason, a holistic understanding of the underlaying working dynamics is highly imperative to proceed forward. In this chapter, we present an overview of the various schemes of mechanisms for various configurations in state-of-the-art low-dimensional electronic and optoelectronic devices for neuromorphic hardware. Also, this chapter provides a forward-looking outlook on the challenges in this emerging field of research to drive next-generation advanced memory technologies for neuromorphic computing.
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Yang, Chaofei, Hai Li, and Yiran Chen. "Nanoscale Memory Architectures for Neuromorphic Computing." In Security Opportunities in Nano Devices and Emerging Technologies, 215–34. CRC Press, 2017. http://dx.doi.org/10.1201/9781315265056-12.
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Ahmed, L. Jubair, S. Dhanasekar, K. Martin Sagayam, Surbhi Vijh, Vipin Tyagi, Mayank Singh, and Alex Norta. "Introduction to Neuromorphic Computing Systems." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–29. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6596-7.ch001.
Abstract:
The process of using electronic circuits to replicate the neurobiological architectures seen in the nervous system is known as neuromorphic engineering, also referred to as neuromorphic computing. These technologies are essential for the future of computing, although most of the work in neuromorphic computing has been focused on hardware development. The execution speed, energy efficiency, accessibility and robustness against local failures are vital advantages of neuromorphic computing over conventional methods. Spiking neural networks are generated using neuromorphic computing. This chapter covers the basic ideas of neuromorphic engineering, neuromorphic computing, and its motivating factors and challenges. Deep learning models are frequently referred to as deep neural networks because deep learning techniques use neural network topologies. Deep learning techniques and their different architectures were also covered in this section. Furthermore, Emerging memory Devices for neuromorphic systems and neuromorphic circuits were illustrated.
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Zhuang, Yanling, Shujuan Liu, and Qiang Zhao. "Organic Resistive Memories for Neuromorphic Electronics." In Advanced Memory Technology, 60–120. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00060.
Abstract:
With the rapid development of big data, advanced data storage technologies with lower power consumption, faster switching speed, higher integration density and larger storage capacity have become the target of storage electronics in the future. However, traditional Si-based CMOS technology and von Neumann architecture will reach their limits, which cannot satisfy the needs of ultra-high density, ultra-small size, and in-memory computing. Due to their low cost, fast speed, easy handling, high energy efficiency, good scalability and flexibility, organic resistive memories are expected to be candidates for high-density storage, logic computing, and neuromorphic computing. In this chapter, we summarize the research progress of organic resistive switching materials and devices. Firstly, the device structure, storage type and switching mechanism are introduced in detail. Secondly, the design strategies and memory properties of various organic resistive switching materials including organic small molecules, organometallic compounds, polymers, and biomaterials are systematically summarized, while the key performance parameters of the memories are also specifically mentioned. Next, the applications of memristors in neuromorphic computing such as artificial synapses, image recognition, and in-memory arithmetic and logic computing are also discussed. Finally, the current challenges and future directions in developing organic resistive memory materials and their neuromorphic devices are outlined.
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Zanotti, Tommaso, Paolo Pavan, and Francesco Maria Puglisi. "Study of RRAM-Based Binarized Neural Networks Inference Accelerators Using an RRAM Physics-Based Compact Model." In Neuromorphic Computing [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110340.
Abstract:
In-memory computing hardware accelerators for binarized neural networks based on resistive RAM (RRAM) memory technologies represent a promising solution for enabling the execution of deep neural network algorithms on resource-constrained devices at the edge of the network. However, the intrinsic stochasticity and nonidealities of RRAM devices can easily lead to unreliable circuit operations if not appropriately considered during the design phase. In this chapter, analysis and design methodologies enabled by RRAM physics-based compact models of LIM and mixed-signal BNN inference accelerators are discussed. As a use case example, the UNIMORE RRAM physics-based compact model calibrated on an RRAM technology from the literature, is used to determine the performance vs. reliability trade-offs of different in-memory computing accelerators: i) a logic-in-memory accelerator based on the material implication logic, ii) a mixed-signal BNN accelerator, and iii) a hybrid accelerator enabling both computing paradigms on the same array. Finally, the performance of the three accelerators on a BNN inference task is compared and benchmarked with the state of the art.
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Pereira, M. E., E. Carlos, E. Fortunato, R. Martins, P. Barquinha, and A. Kiazadeh. "Amorphous Oxide Semiconductor Memristors: Brain-inspired Computation." In Advanced Memory Technology, 431–57. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00431.
Abstract:
Memristors in crossbar arrays can accomplish computing operations while storing data at the same physical location, enabling a cost-efficient latency-free solution to the von Neumann bottleneck. Amorphous oxide semiconductor (AOS)-based memristors can be engineered to perform filamentary- and/or interface-type resistive switching. Their superior characteristics such as high flexibility compatible with low-temperature and easy manufacturing evidence their potential for embedded flexible neuromorphic technologies. In this chapter, the state-of-the-art on AOS-based resistive switching devices is analysed, along with their suitability for specific neuromorphic applications such as in-memory computation and deep and spiking neural networks. Currently, crosstalk is the main obstacle to large-scale crossbar integration and, therefore, the proposed main approaches to overcome this obstacle are discussed. Here, given the high level of behaviour control offered by AOS-based memristors, self-rectifying characteristics or optoelectronic features can be established. Moreover, the compatibility of AOS films with both memristors and thin-film transistors provides the necessary means for active crossbars to be developed in a cost-efficient, simple and higher-interconnectivity manner.
Conference papers on the topic "Neuromorphic technologies/devices":
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Strukov, D. "Emerging Memory Technologies for Neuromorphic Computing." In 2016 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2016. http://dx.doi.org/10.7567/ssdm.2016.b-7-02.
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Shelby, Robert M., Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, Kohji Hosokawa, Scott C. Lewis, and Geoffrey W. Burr. "Neuromorphic technologies for next-generation cognitive computing." In 2017 IEEE Electron Devices Technology and Manufacturing Conference (EDTM). IEEE, 2017. http://dx.doi.org/10.1109/edtm.2017.7947500.
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Lee, Sungsik. "Amorphous oxide thin-film devices for neuromorphic applications." In Advances in Display Technologies XII, edited by Jiun-Haw Lee, Qiong-Hua Wang, and Tae-Hoon Yoon. SPIE, 2022. http://dx.doi.org/10.1117/12.2612015.
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Shastri, Bhavin J., Thomas Ferreira de Lima, Alexander N. Tait, Bicky A. Marquez, Hsuan-Tung Peng, Chaoran Huang, Volker J. Sorger, and Paul R. Prucnal. "Advances in neuromorphic photonics (Conference Presentation)." In Integrated Optics: Devices, Materials, and Technologies XXIV, edited by Sonia M. García-Blanco and Pavel Cheben. SPIE, 2020. http://dx.doi.org/10.1117/12.2554476.
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Offrein, Bert Jan, Tommaso Stecconi, Donato Francesco Falcone, Elger Anne Vlieg, Felix Hermann, Laura Bégon-Lours, Daniel Jubin, et al. "Photonic and electronic integrated technologies for neuromorphic computing." In 2023 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2023. http://dx.doi.org/10.7567/ssdm.2023.h-2-01.
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Dabos, George, George Mourgias-Alexandris, Angelina Totovic, Manos Kirtas, Nikos Passalis, Anastasios Tefas, and Nikos Pleros. "End-to-end deep learning with neuromorphic photonics." In Integrated Optics: Devices, Materials, and Technologies XXV, edited by Sonia M. García-Blanco and Pavel Cheben. SPIE, 2021. http://dx.doi.org/10.1117/12.2587668.
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Polnau, Ernst E., and Mikhail Vorontsov. "Atmospheric turbulence characterization using a neuromorphic camera." In Image Sensing Technologies: Materials, Devices, Systems, and Applications IX, edited by K. Kay Son, Nibir K. Dhar, Achyut K. Dutta, and Sachidananda R. Babu. SPIE, 2022. http://dx.doi.org/10.1117/12.2618894.
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Phillips, Matthew E., Nigel D. Stepp, Jose Cruz-Albrecht, Vincent De Sapio, Tsai-Ching Lu, and Vincent Sritapan. "Neuromorphic and early warning behavior-based authentication for mobile devices." In 2016 IEEE Symposium on Technologies for Homeland Security (HST). IEEE, 2016. http://dx.doi.org/10.1109/ths.2016.7568965.
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Yoo, S. J. Ben. "Intelligent imaging microsystems realized by 3D electronic-photonic integrated circuits with embedded neuromorphic computing." In Image Sensing Technologies: Materials, Devices, Systems, and Applications XI, edited by Nibir K. Dhar, Achyut K. Dutta, and Sachidananda R. Babu. SPIE, 2024. http://dx.doi.org/10.1117/12.3013294.
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Robinson, Michael G., Lin Zhang, Kristina M. Johnson, and David A. Jared. "Custom electro-optic devices for optically implemented neuromorphic computing systems." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mvv9.
Abstract:
Previously we presented optical implementations of neural networks that use parallel optical interconnections between neurons.1 In these systems, the nonlinear neural response is carried out electronically after detecting the optical signal. However, this would be better implemented by means of custom, parallel-optical in–optical/out–electro-optic spatial light modulators (SLM's). Here we present two types of such SLM's fabricated from amorphous silicon (a- Si:H)/FLC and VLSI/FLC technologies. In both cases, the input beam carrying the information from the previous layer of neurons is detected and is transformed into an electric current. This current is then electronically processed, and the resultant voltage is applied across an FLC modulator, which spatially modulates the output optical beam. For the a-Si:H/FLC device, simple linear subtractions between spatially separated optical channels can be achieved, with a nonlinear response obtained through the switching characteristic of the FLC.2 This is particularly applicable in optical implementations that use bipolar interconnection weights.1