Pub Date : 2023-10-31DOI: 10.1088/2634-4386/ad06cb
Adnan Mehonic, Charlotte Frenkel, Eleni Vasilaki
{"title":"Editorial: Focus issue on energy-efficient neuromorphic devices, systems and algorithms","authors":"Adnan Mehonic, Charlotte Frenkel, Eleni Vasilaki","doi":"10.1088/2634-4386/ad06cb","DOIUrl":"https://doi.org/10.1088/2634-4386/ad06cb","url":null,"abstract":"","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139309127","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 : 2023-09-05DOI: 10.1088/2634-4386/acf6db
Elisa Donati, Cecilia Laschi, Barbara Mazzolai, C. Bartolozzi
The NCE Focus Issue on Bioinspired Adaptive Intelligent Robots aims at collecting evidence of the different forms of biomimicry in robotics, from soft robotics and embodiment to neuromorphic sensing, computation and control, as enabling approaches to intelligent and adaptive robots.
{"title":"Editorial: ‘Bioinspired Adaptive Intelligent Robots’","authors":"Elisa Donati, Cecilia Laschi, Barbara Mazzolai, C. Bartolozzi","doi":"10.1088/2634-4386/acf6db","DOIUrl":"https://doi.org/10.1088/2634-4386/acf6db","url":null,"abstract":"The NCE Focus Issue on Bioinspired Adaptive Intelligent Robots aims at collecting evidence of the different forms of biomimicry in robotics, from soft robotics and embodiment to neuromorphic sensing, computation and control, as enabling approaches to intelligent and adaptive robots.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123044415","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 : 2023-08-31DOI: 10.1088/2634-4386/acf1c6
D. Nishioka, T. Tsuchiya, T. Higuchi, K. Terabe
Artificial synapses capable of mimicking the fundamental functionalities of biological synapses are critical to the building of efficient neuromorphic systems. We have developed a H x WO3-based artificial synapse that replicates such synaptic functionalities via an all-solid-state redox transistor mechanism. The subject synaptic-H x WO3 transistor, which operates by current pulse control, exhibits excellent synaptic properties including good linearity, low update variation and conductance modulation characteristics. We investigated the performance of the device under various operating conditions, and the impact of the characteristics of the device on artificial neural network computing. Although the subject synaptic-H x WO3 transistor showed an insufficient recognition accuracy of 66% for a handwritten digit recognition task with voltage pulse control, it achieved an excellent accuracy of 88% with current pulse control, which is approaching the 93% accuracy of an ideal synaptic device. This result suggests that the performance of any redox-transistor-type artificial synapse can be dramatically improved by current pulse control, which in turn paves the way for further exploration and the evolution of advanced neuromorphic systems, with the potential to revolutionize the artificial intelligence domain. It further marks a significant stride towards the realization of high-performance, low-power consumption computing devices.
人工突触能够模仿生物突触的基本功能，这对于构建高效的神经形态系统至关重要。我们开发了一种基于羟基wo3的人工突触，通过全固态氧化还原晶体管机制复制了这种突触功能。本课题的synaptic- h x WO3晶体管通过电流脉冲控制工作，具有良好的线性度、低更新变化和电导调制特性。我们研究了该设备在各种工况下的性能，以及设备特性对人工神经网络计算的影响。虽然被试synaptic- h x WO3晶体管在电压脉冲控制下的手写数字识别任务的识别精度不足66%，但在电流脉冲控制下，它的识别精度达到了88%，接近理想突触器件的93%的精度。这一结果表明，电流脉冲控制可以显著改善任何氧化还原晶体管型人工突触的性能，从而为进一步探索和进化高级神经形态系统铺平了道路，并有可能彻底改变人工智能领域。它进一步标志着向实现高性能、低功耗计算设备迈出了重要的一步。
{"title":"Enhanced synaptic characteristics of H x WO3-based neuromorphic devices, achieved by current pulse control, for artificial neural networks","authors":"D. Nishioka, T. Tsuchiya, T. Higuchi, K. Terabe","doi":"10.1088/2634-4386/acf1c6","DOIUrl":"https://doi.org/10.1088/2634-4386/acf1c6","url":null,"abstract":"Artificial synapses capable of mimicking the fundamental functionalities of biological synapses are critical to the building of efficient neuromorphic systems. We have developed a H x WO3-based artificial synapse that replicates such synaptic functionalities via an all-solid-state redox transistor mechanism. The subject synaptic-H x WO3 transistor, which operates by current pulse control, exhibits excellent synaptic properties including good linearity, low update variation and conductance modulation characteristics. We investigated the performance of the device under various operating conditions, and the impact of the characteristics of the device on artificial neural network computing. Although the subject synaptic-H x WO3 transistor showed an insufficient recognition accuracy of 66% for a handwritten digit recognition task with voltage pulse control, it achieved an excellent accuracy of 88% with current pulse control, which is approaching the 93% accuracy of an ideal synaptic device. This result suggests that the performance of any redox-transistor-type artificial synapse can be dramatically improved by current pulse control, which in turn paves the way for further exploration and the evolution of advanced neuromorphic systems, with the potential to revolutionize the artificial intelligence domain. It further marks a significant stride towards the realization of high-performance, low-power consumption computing devices.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115082128","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 : 2023-08-22DOI: 10.1088/2634-4386/acf2bf
S. Carapezzi, Andrew Plews, G. Boschetto, A. Nejim, S. Karg, A. Todri-Sanial
The frequency of vanadium dioxide (VO2) oscillators is a fundamental figure of merit for the realization of neuromorphic circuits called oscillatory neural networks (ONNs) since the high frequency of oscillators ensures low-power consuming, real-time computing ONNs. In this study, we perform electrothermal 3D technology computer-aided design (TCAD) simulations of a VO2 relaxation oscillator. We find that there exists an upper limit to its operating frequency, where such a limit is not predicted from a purely circuital model of the VO2 oscillator. We investigate the intrinsic physical mechanisms that give rise to this upper limit. Our TCAD simulations show that it arises a dependence on the frequency of the points of the curve current versus voltage across the VO2 device corresponding to the insulator-to-metal transition (IMT) and metal-to-insulator transition (MIT) during oscillation, below some threshold values of Cext . This implies that the condition for the self-oscillatory regime may be satisfied by a given load-line in the low-frequency range but no longer at higher frequencies, with consequent suppression of oscillations. We note that this variation of the IMT/MIT points below some threshold values of Cext is due to a combination of different factors: intermediate resistive states achievable by VO2 channel and the interplay between frequency and heat transfer rate. Although the upper limit on the frequency that we extract is linked to the specific VO2 device we simulate, our findings apply qualitatively to any VO2 oscillator. Overall, our study elucidates the link between electrical and thermal behavior in VO2 devices that sets a constraint on the upper values of the operating frequency of any VO2 oscillator.
{"title":"How fast can vanadium dioxide neuron-mimicking devices oscillate? Physical mechanisms limiting the frequency of vanadium dioxide oscillators","authors":"S. Carapezzi, Andrew Plews, G. Boschetto, A. Nejim, S. Karg, A. Todri-Sanial","doi":"10.1088/2634-4386/acf2bf","DOIUrl":"https://doi.org/10.1088/2634-4386/acf2bf","url":null,"abstract":"The frequency of vanadium dioxide (VO2) oscillators is a fundamental figure of merit for the realization of neuromorphic circuits called oscillatory neural networks (ONNs) since the high frequency of oscillators ensures low-power consuming, real-time computing ONNs. In this study, we perform electrothermal 3D technology computer-aided design (TCAD) simulations of a VO2 relaxation oscillator. We find that there exists an upper limit to its operating frequency, where such a limit is not predicted from a purely circuital model of the VO2 oscillator. We investigate the intrinsic physical mechanisms that give rise to this upper limit. Our TCAD simulations show that it arises a dependence on the frequency of the points of the curve current versus voltage across the VO2 device corresponding to the insulator-to-metal transition (IMT) and metal-to-insulator transition (MIT) during oscillation, below some threshold values of Cext . This implies that the condition for the self-oscillatory regime may be satisfied by a given load-line in the low-frequency range but no longer at higher frequencies, with consequent suppression of oscillations. We note that this variation of the IMT/MIT points below some threshold values of Cext is due to a combination of different factors: intermediate resistive states achievable by VO2 channel and the interplay between frequency and heat transfer rate. Although the upper limit on the frequency that we extract is linked to the specific VO2 device we simulate, our findings apply qualitatively to any VO2 oscillator. Overall, our study elucidates the link between electrical and thermal behavior in VO2 devices that sets a constraint on the upper values of the operating frequency of any VO2 oscillator.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128916795","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 : 2023-08-16DOI: 10.1088/2634-4386/acf0e4
Ram Singh Yadav, Aniket Sadashiva, Amod Holla, P. Muduli, D. Bhowmik
Topological-soliton-based devices, like the ferromagnetic domain-wall device, have been proposed as non-volatile memory (NVM) synapses in electronic crossbar arrays for fast and energy-efficient implementation of on-chip learning of neural networks (NN). High linearity and symmetry in the synaptic weight-update characteristic of the device (long-term potentiation (LTP) and long-term depression (LTD)) are important requirements to obtain high classification/regression accuracy in such an on-chip learning scheme. However, obtaining such linear and symmetric LTP and LTD characteristics in the ferromagnetic domain-wall device has remained a challenge. Here, we first carry out micromagnetic simulations of the device to show that the incorporation of defects at the edges of the device, with the defects having higher perpendicular magnetic anisotropy compared to the rest of the ferromagnetic layer, leads to massive improvement in the linearity and symmetry of the LTP and LTD characteristics of the device. This is because these defects act as pinning centres for the domain wall and prevent it from moving during the delay time between two consecutive programming current pulses, which is not the case when the device does not have defects. Next, we carry out system-level simulations of two crossbar arrays with synaptic characteristics of domain-wall synapse devices incorporated in them: one without such defects, and one with such defects. For on-chip learning of both long short-term memory networks (using a regression task) and fully connected NN (using a classification task), we show improved performance when the domain-wall synapse devices have defects at the edges. We also estimate the energy consumption in these synaptic devices and project their scaling, with respect to on-chip learning in corresponding crossbar arrays.
{"title":"Impact of edge defects on the synaptic characteristic of a ferromagnetic domain-wall device and on on-chip learning","authors":"Ram Singh Yadav, Aniket Sadashiva, Amod Holla, P. Muduli, D. Bhowmik","doi":"10.1088/2634-4386/acf0e4","DOIUrl":"https://doi.org/10.1088/2634-4386/acf0e4","url":null,"abstract":"Topological-soliton-based devices, like the ferromagnetic domain-wall device, have been proposed as non-volatile memory (NVM) synapses in electronic crossbar arrays for fast and energy-efficient implementation of on-chip learning of neural networks (NN). High linearity and symmetry in the synaptic weight-update characteristic of the device (long-term potentiation (LTP) and long-term depression (LTD)) are important requirements to obtain high classification/regression accuracy in such an on-chip learning scheme. However, obtaining such linear and symmetric LTP and LTD characteristics in the ferromagnetic domain-wall device has remained a challenge. Here, we first carry out micromagnetic simulations of the device to show that the incorporation of defects at the edges of the device, with the defects having higher perpendicular magnetic anisotropy compared to the rest of the ferromagnetic layer, leads to massive improvement in the linearity and symmetry of the LTP and LTD characteristics of the device. This is because these defects act as pinning centres for the domain wall and prevent it from moving during the delay time between two consecutive programming current pulses, which is not the case when the device does not have defects. Next, we carry out system-level simulations of two crossbar arrays with synaptic characteristics of domain-wall synapse devices incorporated in them: one without such defects, and one with such defects. For on-chip learning of both long short-term memory networks (using a regression task) and fully connected NN (using a classification task), we show improved performance when the domain-wall synapse devices have defects at the edges. We also estimate the energy consumption in these synaptic devices and project their scaling, with respect to on-chip learning in corresponding crossbar arrays.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124046743","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 : 2023-08-09DOI: 10.1088/2634-4386/aceea9
Yuhan Shi, Sangheon Oh, Jaeseoung Park, J. D. Valle, Pavel Salev, Ivan K. Schuller, D. Kuzum
In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.
{"title":"Integration of Ag-CBRAM crossbars and Mott ReLU neurons for efficient implementation of deep neural networks in hardware","authors":"Yuhan Shi, Sangheon Oh, Jaeseoung Park, J. D. Valle, Pavel Salev, Ivan K. Schuller, D. Kuzum","doi":"10.1088/2634-4386/aceea9","DOIUrl":"https://doi.org/10.1088/2634-4386/aceea9","url":null,"abstract":"In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567655","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 : 2023-08-08DOI: 10.1088/2634-4386/acee1a
M. Payvand, E. Neftci, Friedemann Zenke
NA
{"title":"Editorial: Focus issue on machine learning for neuromorphic engineering","authors":"M. Payvand, E. Neftci, Friedemann Zenke","doi":"10.1088/2634-4386/acee1a","DOIUrl":"https://doi.org/10.1088/2634-4386/acee1a","url":null,"abstract":"\u0000 <jats:p>NA</jats:p>","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123348602","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 : 2023-07-24DOI: 10.1088/2634-4386/ace9f5
Corentin Delacour, S. Carapezzi, G. Boschetto, Madeleine Abernot, Thierry Gil, N. Azémard, A. Todri-Sanial
Digital electronics based on von Neumann’s architecture is reaching its limits to solve large-scale problems essentially due to the memory fetching. Instead, recent efforts to bring the memory near the computation have enabled highly parallel computations at low energy costs. Oscillatory neural network (ONN) is one example of in-memory analog computing paradigm consisting of coupled oscillating neurons. When implemented in hardware, ONNs naturally perform gradient descent of an energy landscape which makes them particularly suited for solving optimization problems. Although the ONN computational capability and its link with the Ising model are known for decades, implementing a large-scale ONN remains difficult. Beyond the oscillators’ variations, there are still design challenges such as having compact, programmable synapses and a modular architecture for solving large problem instances. In this paper, we propose a mixed-signal architecture named Saturated Kuramoto ONN (SKONN) that leverages both analog and digital domains for efficient ONN hardware implementation. SKONN computes in the analog phase domain while propagating the information digitally to facilitate scaling up the ONN size. SKONN’s separation between computation and propagation enhances the robustness and enables a feed-forward phase propagation that is showcased for the first time. Moreover, the SKONN architecture leads to unique binarizing dynamics that are particularly suitable for solving NP-hard combinatorial optimization problems such as finding the weighted Max-cut of a graph. We find that SKONN’s accuracy is as good as the Goemans–Williamson 0.878-approximation algorithm for Max-cut; whereas SKONN’s computation time only grows logarithmically. We report on Weighted Max-cut experiments using a 9-neuron SKONN proof-of-concept on a printed circuit board (PCB). Finally, we present a low-power 16-neuron SKONN integrated circuit and illustrate SKONN’s feed-forward ability while computing the XOR function.
{"title":"A mixed-signal oscillatory neural network for scalable analog computations in phase domain","authors":"Corentin Delacour, S. Carapezzi, G. Boschetto, Madeleine Abernot, Thierry Gil, N. Azémard, A. Todri-Sanial","doi":"10.1088/2634-4386/ace9f5","DOIUrl":"https://doi.org/10.1088/2634-4386/ace9f5","url":null,"abstract":"Digital electronics based on von Neumann’s architecture is reaching its limits to solve large-scale problems essentially due to the memory fetching. Instead, recent efforts to bring the memory near the computation have enabled highly parallel computations at low energy costs. Oscillatory neural network (ONN) is one example of in-memory analog computing paradigm consisting of coupled oscillating neurons. When implemented in hardware, ONNs naturally perform gradient descent of an energy landscape which makes them particularly suited for solving optimization problems. Although the ONN computational capability and its link with the Ising model are known for decades, implementing a large-scale ONN remains difficult. Beyond the oscillators’ variations, there are still design challenges such as having compact, programmable synapses and a modular architecture for solving large problem instances. In this paper, we propose a mixed-signal architecture named Saturated Kuramoto ONN (SKONN) that leverages both analog and digital domains for efficient ONN hardware implementation. SKONN computes in the analog phase domain while propagating the information digitally to facilitate scaling up the ONN size. SKONN’s separation between computation and propagation enhances the robustness and enables a feed-forward phase propagation that is showcased for the first time. Moreover, the SKONN architecture leads to unique binarizing dynamics that are particularly suitable for solving NP-hard combinatorial optimization problems such as finding the weighted Max-cut of a graph. We find that SKONN’s accuracy is as good as the Goemans–Williamson 0.878-approximation algorithm for Max-cut; whereas SKONN’s computation time only grows logarithmically. We report on Weighted Max-cut experiments using a 9-neuron SKONN proof-of-concept on a printed circuit board (PCB). Finally, we present a low-power 16-neuron SKONN integrated circuit and illustrate SKONN’s feed-forward ability while computing the XOR function.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129000715","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 : 2023-07-21DOI: 10.1088/2634-4386/ace991
R. Legenstein, A. Basu, P. Panda
Neuromorphic computing provides a promising energy-efficient alternative to von-Neumann-type computing and learning architectures. However, the best neuromorphic hardware is useless without suitable inference and learning algorithms that can fully exploit hardware advantages. Such algorithms often have to deal with challenging constraints posed by neuromorphic hardware such as massive parallelism, sparse asynchronous communication, and analog and/or unreliable computing elements. This Focus Issue presents advances on various aspects of algorithms for neuromorphic computing. The collection of articles covers a wide range from very fundamental questions about the computational properties of the basic computing elements in neuromorphic systems, algorithms for continual learning, semantic segmentation, and novel efficient learning paradigms, up to algorithms for a specific application domain.
{"title":"Editorial: Focus on algorithms for neuromorphic computing","authors":"R. Legenstein, A. Basu, P. Panda","doi":"10.1088/2634-4386/ace991","DOIUrl":"https://doi.org/10.1088/2634-4386/ace991","url":null,"abstract":"Neuromorphic computing provides a promising energy-efficient alternative to von-Neumann-type computing and learning architectures. However, the best neuromorphic hardware is useless without suitable inference and learning algorithms that can fully exploit hardware advantages. Such algorithms often have to deal with challenging constraints posed by neuromorphic hardware such as massive parallelism, sparse asynchronous communication, and analog and/or unreliable computing elements. This Focus Issue presents advances on various aspects of algorithms for neuromorphic computing. The collection of articles covers a wide range from very fundamental questions about the computational properties of the basic computing elements in neuromorphic systems, algorithms for continual learning, semantic segmentation, and novel efficient learning paradigms, up to algorithms for a specific application domain.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134561365","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 : 2023-07-07DOI: 10.1088/2634-4386/ace549
Nicolai Timon Bechler, J. Masell
Magnetic textures are promising candidates for unconventional computing due to their non-linear dynamics. We propose to investigate the rich variety of seemingly trivial lamellar magnetic phases, e.g. helical, spiral, stripy phase, or other one-dimensional soliton lattices. These are the natural stray field-free ground states of almost every magnet. The order parameters of these phases may be of potential interest for both classical and unconventional computing, which we refer to as helitronics. For the particular case of a chiral magnet and its helical phase, we use micromagnetic simulations to demonstrate the working principles of all-electrical (i) classical binary memory cells and (ii) memristors and artificial synapses, based on the orientation of the helical stripes.
{"title":"Helitronics as a potential building block for classical and unconventional computing","authors":"Nicolai Timon Bechler, J. Masell","doi":"10.1088/2634-4386/ace549","DOIUrl":"https://doi.org/10.1088/2634-4386/ace549","url":null,"abstract":"Magnetic textures are promising candidates for unconventional computing due to their non-linear dynamics. We propose to investigate the rich variety of seemingly trivial lamellar magnetic phases, e.g. helical, spiral, stripy phase, or other one-dimensional soliton lattices. These are the natural stray field-free ground states of almost every magnet. The order parameters of these phases may be of potential interest for both classical and unconventional computing, which we refer to as helitronics. For the particular case of a chiral magnet and its helical phase, we use micromagnetic simulations to demonstrate the working principles of all-electrical (i) classical binary memory cells and (ii) memristors and artificial synapses, based on the orientation of the helical stripes.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115176977","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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