Pub Date : 2023-07-05DOI: 10.1088/2634-4386/ace473
Cory E. Merkel
Biological intelligence imparts organisms with the ability to overcome a number of key challenges such as adapting to dynamic environments, learning from experience, and making complex decisions, even within a daunting set of constraints (e.g. extremely limited energy). Interestingly, we are encountering several analogous challenges and constraints as artificial intelligence (AI) begins to move from the cloud to the edge in the ever-growing internet-of-things (IoT). Neuromorphic computing is poised to play a critical role in moving AI to the edge, as it enables the implementation of state-of-the-art machine learning algorithms (e.g. deep neural networks) on hardware platforms with limited resources (energy, precision, I/O, etc.). This NCE focus issue on Extreme Edge Computing brings together a variety of works that are aimed at designing neuromorphic computing for AI at-the-edge. The collection includes four original research articles and one topical review paper, which are briefly summarized below
{"title":"NCE focus issue: extreme edge computing","authors":"Cory E. Merkel","doi":"10.1088/2634-4386/ace473","DOIUrl":"https://doi.org/10.1088/2634-4386/ace473","url":null,"abstract":"\u0000 Biological intelligence imparts organisms with the ability to overcome a number of key challenges such as adapting to dynamic environments, learning from experience, and making complex decisions, even within a daunting set of constraints (e.g. extremely limited energy). Interestingly, we are encountering several analogous challenges and constraints as artificial intelligence (AI) begins to move from the cloud to the edge in the ever-growing internet-of-things (IoT). Neuromorphic computing is poised to play a critical role in moving AI to the edge, as it enables the implementation of state-of-the-art machine learning algorithms (e.g. deep neural networks) on hardware platforms with limited resources (energy, precision, I/O, etc.). This NCE focus issue on Extreme Edge Computing brings together a variety of works that are aimed at designing neuromorphic computing for AI at-the-edge. The collection includes four original research articles and one topical review paper, which are briefly summarized below","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129197661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-16DOI: 10.1088/2634-4386/acdf17
B. Romeira, R. Adão, J. Nieder, Q. Al-Taai, Weikang Zhang, R. Hadfield, E. Wasige, M. Hejda, Antonio Hurtado, Ekaterina Malysheva, V. Calzadilla, J. Lourenço, David Marreiros de Castro Alves, J. Figueiredo, I. Ortega-Piwonka, J. Javaloyes, S. Edwards, J. I. Davies, F. Horst, B. Offrein
Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III–V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.
{"title":"Brain-inspired nanophotonic spike computing: challenges and prospects","authors":"B. Romeira, R. Adão, J. Nieder, Q. Al-Taai, Weikang Zhang, R. Hadfield, E. Wasige, M. Hejda, Antonio Hurtado, Ekaterina Malysheva, V. Calzadilla, J. Lourenço, David Marreiros de Castro Alves, J. Figueiredo, I. Ortega-Piwonka, J. Javaloyes, S. Edwards, J. I. Davies, F. Horst, B. Offrein","doi":"10.1088/2634-4386/acdf17","DOIUrl":"https://doi.org/10.1088/2634-4386/acdf17","url":null,"abstract":"Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III–V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131498727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1088/2634-4386/acdbe5
M. Buttberg, I. Valov, S. Menzel
Electrochemical metallization (ECM) cells are based on the principle of voltage controlled formation or dissolution of a nanometer-thin metallic conductive filament (CF) between two electrodes separated by an insulating material, e.g. an oxide. The lifetime of the CF depends on factors such as materials and biasing. Depending on the lifetime of the CF—from microseconds to years—ECM cells show promising properties for use in neuromorphic circuits, for in-memory computing, or as selectors and memory cells in storage applications. For enabling those technologies with ECM cells, the lifetime of the CF has to be controlled. As various authors connect the lifetime with the morphology of the CF, the key parameters for CF formation have to be identified. In this work, we present a 2D axisymmetric physical continuum model that describes the kinetics of volatile and non-volatile ECM cells, as well as the morphology of the CF. It is shown that the morphology depends on both the amplitude of the applied voltage signal and CF-growth induced mechanical stress within the oxide layer. The model is validated with previously published kinetic measurements of non-volatile Ag/SiO2/Pt and volatile Ag/HfO2/Pt cells and the simulated CF morphologies are consistent with previous experimental CF observations.
{"title":"Simulating the filament morphology in electrochemical metallization cells","authors":"M. Buttberg, I. Valov, S. Menzel","doi":"10.1088/2634-4386/acdbe5","DOIUrl":"https://doi.org/10.1088/2634-4386/acdbe5","url":null,"abstract":"Electrochemical metallization (ECM) cells are based on the principle of voltage controlled formation or dissolution of a nanometer-thin metallic conductive filament (CF) between two electrodes separated by an insulating material, e.g. an oxide. The lifetime of the CF depends on factors such as materials and biasing. Depending on the lifetime of the CF—from microseconds to years—ECM cells show promising properties for use in neuromorphic circuits, for in-memory computing, or as selectors and memory cells in storage applications. For enabling those technologies with ECM cells, the lifetime of the CF has to be controlled. As various authors connect the lifetime with the morphology of the CF, the key parameters for CF formation have to be identified. In this work, we present a 2D axisymmetric physical continuum model that describes the kinetics of volatile and non-volatile ECM cells, as well as the morphology of the CF. It is shown that the morphology depends on both the amplitude of the applied voltage signal and CF-growth induced mechanical stress within the oxide layer. The model is validated with previously published kinetic measurements of non-volatile Ag/SiO2/Pt and volatile Ag/HfO2/Pt cells and the simulated CF morphologies are consistent with previous experimental CF observations.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123429868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1088/2634-4386/acdaba
Thorben Schoepe, Daniel Gutierrez-Galan, J. P. Dominguez-Morales, Hugh Greatorex, Angel Francisco Jiménez Fernández, A. Linares-Barranco, E. Chicca
Sound source localization (SSL) is used in various applications such as industrial noise-control, speech detection in mobile phones, speech enhancement in hearing aids and many more. Newest video conferencing setups use SSL. The position of a speaker is detected from the difference in the audio waves received by a microphone array. After detection the camera focuses onto the location of the speaker. The human brain is also able to detect the location of a speaker from auditory signals. It uses, among other cues, the difference in amplitude and arrival time of the sound wave at the two ears, called interaural level and time difference. However, the substrate and computational primitives of our brain are different from classical digital computing. Due to its low power consumption of around 20 W and its performance in real time the human brain has become a great source of inspiration for emerging technologies. One of these technologies is neuromorphic hardware which implements the fundamental principles of brain computing identified until today using complementary metal-oxide-semiconductor technologies and new devices. In this work we propose the first neuromorphic closed-loop robotic system that uses the interaural time difference for SSL in real time. Our system can successfully locate sound sources such as human speech. In a closed-loop experiment, the robotic platform turned immediately into the direction of the sound source with a turning velocity linearly proportional to the angle difference between sound source and binaural microphones. After this initial turn, the robotic platform remains at the direction of the sound source. Even though the system only uses very few resources of the available hardware, consumes around 1 W, and was only tuned by hand, meaning it does not contain any learning at all, it already reaches performances comparable to other neuromorphic approaches. The SSL system presented in this article brings us one step closer towards neuromorphic event-based systems for robotics and embodied computing.
{"title":"Closed-loop sound source localization in neuromorphic systems","authors":"Thorben Schoepe, Daniel Gutierrez-Galan, J. P. Dominguez-Morales, Hugh Greatorex, Angel Francisco Jiménez Fernández, A. Linares-Barranco, E. Chicca","doi":"10.1088/2634-4386/acdaba","DOIUrl":"https://doi.org/10.1088/2634-4386/acdaba","url":null,"abstract":"Sound source localization (SSL) is used in various applications such as industrial noise-control, speech detection in mobile phones, speech enhancement in hearing aids and many more. Newest video conferencing setups use SSL. The position of a speaker is detected from the difference in the audio waves received by a microphone array. After detection the camera focuses onto the location of the speaker. The human brain is also able to detect the location of a speaker from auditory signals. It uses, among other cues, the difference in amplitude and arrival time of the sound wave at the two ears, called interaural level and time difference. However, the substrate and computational primitives of our brain are different from classical digital computing. Due to its low power consumption of around 20 W and its performance in real time the human brain has become a great source of inspiration for emerging technologies. One of these technologies is neuromorphic hardware which implements the fundamental principles of brain computing identified until today using complementary metal-oxide-semiconductor technologies and new devices. In this work we propose the first neuromorphic closed-loop robotic system that uses the interaural time difference for SSL in real time. Our system can successfully locate sound sources such as human speech. In a closed-loop experiment, the robotic platform turned immediately into the direction of the sound source with a turning velocity linearly proportional to the angle difference between sound source and binaural microphones. After this initial turn, the robotic platform remains at the direction of the sound source. Even though the system only uses very few resources of the available hardware, consumes around 1 W, and was only tuned by hand, meaning it does not contain any learning at all, it already reaches performances comparable to other neuromorphic approaches. The SSL system presented in this article brings us one step closer towards neuromorphic event-based systems for robotics and embodied computing.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132638542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.1088/2634-4386/acd952
Ahmad El Ferdaoussi, J. Rouat, É. Plourde
Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency metrics based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency metrics are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike Coding, Send-on-Delta coding, Ben’s Spiker Algorithm, and Leaky Integrate-and-Fire (LIF) coding. The results show that LIF coding has the overall best performance in terms of coding, computational, and energy efficiency.
{"title":"Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory","authors":"Ahmad El Ferdaoussi, J. Rouat, É. Plourde","doi":"10.1088/2634-4386/acd952","DOIUrl":"https://doi.org/10.1088/2634-4386/acd952","url":null,"abstract":"Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency metrics based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency metrics are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike Coding, Send-on-Delta coding, Ben’s Spiker Algorithm, and Leaky Integrate-and-Fire (LIF) coding. The results show that LIF coding has the overall best performance in terms of coding, computational, and energy efficiency.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134072711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.1088/2634-4386/accd8f
Horst Petschenig, R. Legenstein
Mixed-signal and fully digital neuromorphic systems have been of significant interest for deploying spiking neural networks in an energy-efficient manner. However, many of these systems impose constraints in terms of fan-in, memory, or synaptic weight precision that have to be considered during network design and training. In this paper, we present quantized rewiring (Q-rewiring), an algorithm that can train both spiking and non-spiking neural networks while meeting hardware constraints during the entire training process. To demonstrate our approach, we train both feedforward and recurrent neural networks with a combined fan-in/weight precision limit, a constraint that is, for example, present in the DYNAP-SE mixed-signal analog/digital neuromorphic processor. Q-rewiring simultaneously performs quantization and rewiring of synapses and synaptic weights through gradient descent updates and projecting the trainable parameters to a constraint-compliant region. Using our algorithm, we find trade-offs between the number of incoming connections to neurons and network performance for a number of common benchmark datasets.
{"title":"Quantized rewiring: hardware-aware training of sparse deep neural networks","authors":"Horst Petschenig, R. Legenstein","doi":"10.1088/2634-4386/accd8f","DOIUrl":"https://doi.org/10.1088/2634-4386/accd8f","url":null,"abstract":"Mixed-signal and fully digital neuromorphic systems have been of significant interest for deploying spiking neural networks in an energy-efficient manner. However, many of these systems impose constraints in terms of fan-in, memory, or synaptic weight precision that have to be considered during network design and training. In this paper, we present quantized rewiring (Q-rewiring), an algorithm that can train both spiking and non-spiking neural networks while meeting hardware constraints during the entire training process. To demonstrate our approach, we train both feedforward and recurrent neural networks with a combined fan-in/weight precision limit, a constraint that is, for example, present in the DYNAP-SE mixed-signal analog/digital neuromorphic processor. Q-rewiring simultaneously performs quantization and rewiring of synapses and synaptic weights through gradient descent updates and projecting the trainable parameters to a constraint-compliant region. Using our algorithm, we find trade-offs between the number of incoming connections to neurons and network performance for a number of common benchmark datasets.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"102 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123166385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-23DOI: 10.1088/2634-4386/acd80b
S. Slesazeck, T. Mikolajick
Our pathway towards intelligent computing systems leads to an ever-increasing amount of data movement, which apparently pushes the conventional von-Neumann architecture towards its limits in terms of performance and energy consumption. Combining computing and storage functionality locally within one structure is seen as one potential branch to bypass this issue. Neural Networks are a very promising path in that direction. Artificial neural networks rely to a large extend on “synaptic weight” storage and multiply-accumulate (MAC) functionality in either digital or analogue way, the latter one making use of Kirchhoff’s and Ohm’s law. The brain inspired neuromorphic computing architectures go one step further and more directly mimic the key biological elements of the brain: neurons and synapses. For hardware realization of such neuromorphic computing architectures the availability of scalable non-volatile memory devices to realize high density synapses for deep learning artificial neural networks or brain inspired spiking neural networks is essential. This NCE Focus Issue concentrates on the discussion of hafnium oxide based neuromorphic devices. Hafnium oxide has become a standard dielectric material in complementary metal oxide semiconductor (CMOS) fabrication processes since its introduction as gate dielectric for metal oxide semiconductor field effect Transistors (MOSFETs) back in 2007. Since then, its possible application field has significantly widened into the usage as memory devices. It was shown that valence change based resistive switching devices, better known as either resistive random-access memory (RRAM) or Memristor, could be realized with good properties using hafnium oxide. Moreover, in 2011 it was reported that under special conditions hafnium oxide can even be transformed into a ferroelectric. The latter enables a variety of different types of memory cells, namely capacitor based ferroelectric random access memories (FeRAM), ferroelectric field effect transistors (FeFET) and ferroelectric tunneling junctions (FTJ). This special issue will cover all aspects of using hafnium oxide based devices in neuromorphic systems starting from the material optimization via device concepts and modeling towards simulation and integration of neuromorphic systems.
{"title":"Focus issue on hafnium oxide based neuromorphic devices","authors":"S. Slesazeck, T. Mikolajick","doi":"10.1088/2634-4386/acd80b","DOIUrl":"https://doi.org/10.1088/2634-4386/acd80b","url":null,"abstract":"\u0000 Our pathway towards intelligent computing systems leads to an ever-increasing amount of data movement, which apparently pushes the conventional von-Neumann architecture towards its limits in terms of performance and energy consumption. Combining computing and storage functionality locally within one structure is seen as one potential branch to bypass this issue. Neural Networks are a very promising path in that direction. Artificial neural networks rely to a large extend on “synaptic weight” storage and multiply-accumulate (MAC) functionality in either digital or analogue way, the latter one making use of Kirchhoff’s and Ohm’s law. The brain inspired neuromorphic computing architectures go one step further and more directly mimic the key biological elements of the brain: neurons and synapses. For hardware realization of such neuromorphic computing architectures the availability of scalable non-volatile memory devices to realize high density synapses for deep learning artificial neural networks or brain inspired spiking neural networks is essential. This NCE Focus Issue concentrates on the discussion of hafnium oxide based neuromorphic devices. Hafnium oxide has become a standard dielectric material in complementary metal oxide semiconductor (CMOS) fabrication processes since its introduction as gate dielectric for metal oxide semiconductor field effect Transistors (MOSFETs) back in 2007. Since then, its possible application field has significantly widened into the usage as memory devices. It was shown that valence change based resistive switching devices, better known as either resistive random-access memory (RRAM) or Memristor, could be realized with good properties using hafnium oxide. Moreover, in 2011 it was reported that under special conditions hafnium oxide can even be transformed into a ferroelectric. The latter enables a variety of different types of memory cells, namely capacitor based ferroelectric random access memories (FeRAM), ferroelectric field effect transistors (FeFET) and ferroelectric tunneling junctions (FTJ). This special issue will cover all aspects of using hafnium oxide based devices in neuromorphic systems starting from the material optimization via device concepts and modeling towards simulation and integration of neuromorphic systems.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"286 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133138319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.1088/2634-4386/acd6b3
Davide Cipollini, Lambert Schomaker
To build neuromorphic hardware with self-assembled memristive networks, it is necessary to determine how the functional connectivity between electrodes can be adjusted, under the application of external signals. In this work, we analyse a model of a disordered memristor-resistor network, within the framework of graph theory. Such a model is well suited for the simulation of physical self-assembled neuromorphic materials where impurities are likely to be present. Two primary mechanisms that modulate the collective dynamics are investigated: the strength of interaction, i.e. the ratio of the two limiting conductance states of the memristive components, and the role of disorder in the form of density of Ohmic conductors (OCs) diluting the network. We consider the case where a fraction of the network edges has memristive properties, while the remaining part shows pure Ohmic behaviour. We consider both the case of poor and good OCs. Both the role of the interaction strength and the presence of OCs are investigated in relation to the trace formation between electrodes at the fixed point of the dynamics. The latter is analysed through an ideal observer approach. Thus, network entropy is used to understand the self-reinforcing and cooperative inhibition of other memristive elements resulting in the formation of a winner-take-all path. Both the low interaction strength and the dilution of the memristive fraction in a network provide a reduction of the steep non-linearity in the network conductance under the application of a steady input voltage. Entropy analysis shows enhanced robustness in selective trace formation to the applied voltage for heterogeneous networks of memristors diluted by poor OCs in the vicinity of the percolation threshold. The input voltage controls the diversity in trace formation.
{"title":"Conduction and entropy analysis of a mixed memristor-resistor model for neuromorphic networks","authors":"Davide Cipollini, Lambert Schomaker","doi":"10.1088/2634-4386/acd6b3","DOIUrl":"https://doi.org/10.1088/2634-4386/acd6b3","url":null,"abstract":"To build neuromorphic hardware with self-assembled memristive networks, it is necessary to determine how the functional connectivity between electrodes can be adjusted, under the application of external signals. In this work, we analyse a model of a disordered memristor-resistor network, within the framework of graph theory. Such a model is well suited for the simulation of physical self-assembled neuromorphic materials where impurities are likely to be present. Two primary mechanisms that modulate the collective dynamics are investigated: the strength of interaction, i.e. the ratio of the two limiting conductance states of the memristive components, and the role of disorder in the form of density of Ohmic conductors (OCs) diluting the network. We consider the case where a fraction of the network edges has memristive properties, while the remaining part shows pure Ohmic behaviour. We consider both the case of poor and good OCs. Both the role of the interaction strength and the presence of OCs are investigated in relation to the trace formation between electrodes at the fixed point of the dynamics. The latter is analysed through an ideal observer approach. Thus, network entropy is used to understand the self-reinforcing and cooperative inhibition of other memristive elements resulting in the formation of a winner-take-all path. Both the low interaction strength and the dilution of the memristive fraction in a network provide a reduction of the steep non-linearity in the network conductance under the application of a steady input voltage. Entropy analysis shows enhanced robustness in selective trace formation to the applied voltage for heterogeneous networks of memristors diluted by poor OCs in the vicinity of the percolation threshold. The input voltage controls the diversity in trace formation.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116511111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1088/2634-4386/acd4e2
M. Pereira, R. Martins, E. Fortunato, P. Barquinha, A. Kiazadeh
Neuromorphic computing has been gaining momentum for the past decades and has been appointed as the replacer of the outworn technology in conventional computing systems. Artificial neural networks (ANNs) can be composed by memristor crossbars in hardware and perform in-memory computing and storage, in a power, cost and area efficient way. In optoelectronic memristors (OEMs), resistive switching (RS) can be controlled by both optical and electronic signals. Using light as synaptic weigh modulator provides a high-speed non-destructive method, not dependent on electrical wires, that solves crosstalk issues. In particular, in artificial visual systems, OEMs can act as the artificial retina and combine optical sensing and high-level image processing. Therefore, several efforts have been made by the scientific community into developing OEMs that can meet the demands of each specific application. In this review, the recent advances in inorganic OEMs are summarized and discussed. The engineering of the device structure provides the means to manipulate RS performance and, thus, a comprehensive analysis is performed regarding the already proposed memristor materials structure and their specific characteristics. Moreover, their potential applications in logic gates, ANNs and, in more detail, on artificial visual systems are also assessed, taking into account the figures of merit described so far.
{"title":"Recent progress in optoelectronic memristors for neuromorphic and in-memory computation","authors":"M. Pereira, R. Martins, E. Fortunato, P. Barquinha, A. Kiazadeh","doi":"10.1088/2634-4386/acd4e2","DOIUrl":"https://doi.org/10.1088/2634-4386/acd4e2","url":null,"abstract":"Neuromorphic computing has been gaining momentum for the past decades and has been appointed as the replacer of the outworn technology in conventional computing systems. Artificial neural networks (ANNs) can be composed by memristor crossbars in hardware and perform in-memory computing and storage, in a power, cost and area efficient way. In optoelectronic memristors (OEMs), resistive switching (RS) can be controlled by both optical and electronic signals. Using light as synaptic weigh modulator provides a high-speed non-destructive method, not dependent on electrical wires, that solves crosstalk issues. In particular, in artificial visual systems, OEMs can act as the artificial retina and combine optical sensing and high-level image processing. Therefore, several efforts have been made by the scientific community into developing OEMs that can meet the demands of each specific application. In this review, the recent advances in inorganic OEMs are summarized and discussed. The engineering of the device structure provides the means to manipulate RS performance and, thus, a comprehensive analysis is performed regarding the already proposed memristor materials structure and their specific characteristics. Moreover, their potential applications in logic gates, ANNs and, in more detail, on artificial visual systems are also assessed, taking into account the figures of merit described so far.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126439053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-03DOI: 10.1088/2634-4386/acd20d
M. R. E. U. Shougat, E. Perkins
The van der Pol oscillator has historical and practical significance to spiking neural networks. It was proposed as one of the first models for heart oscillations, and it has been used as the building block for spiking neural networks. Furthermore, the van der Pol oscillator is also readily implemented as an electronic circuit. For these reasons, we chose to implement the van der Pol oscillator as a physical reservoir computer (PRC) to highlight its computational ability, even when it is not in an array. The van der Pol PRC is explored using various logical tasks with numerical simulations, and a field-programmable analog array circuit for the van der Pol system is constructed to verify its use as a reservoir computer. As the van der Pol oscillator can be easily constructed with commercial-off-the-shelf circuit components, this PRC could be a viable option for computing on edge devices. We believe this is the first time that the van der Pol oscillator has been demonstrated as a PRC.
{"title":"The van der Pol physical reservoir computer","authors":"M. R. E. U. Shougat, E. Perkins","doi":"10.1088/2634-4386/acd20d","DOIUrl":"https://doi.org/10.1088/2634-4386/acd20d","url":null,"abstract":"The van der Pol oscillator has historical and practical significance to spiking neural networks. It was proposed as one of the first models for heart oscillations, and it has been used as the building block for spiking neural networks. Furthermore, the van der Pol oscillator is also readily implemented as an electronic circuit. For these reasons, we chose to implement the van der Pol oscillator as a physical reservoir computer (PRC) to highlight its computational ability, even when it is not in an array. The van der Pol PRC is explored using various logical tasks with numerical simulations, and a field-programmable analog array circuit for the van der Pol system is constructed to verify its use as a reservoir computer. As the van der Pol oscillator can be easily constructed with commercial-off-the-shelf circuit components, this PRC could be a viable option for computing on edge devices. We believe this is the first time that the van der Pol oscillator has been demonstrated as a PRC.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114041478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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