Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions...Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.展开更多
Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuro...Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.展开更多
In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spa...In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spatio-temporal information.However, the non-differential spike activity makes SNNs more difficult to train in supervised training. Most existing methods focusing on introducing an approximated derivative to replace it, while they are often based on static surrogate functions. In this paper, we propose a progressive surrogate gradient learning for backpropagation of SNNs, which is able to approximate the step function gradually and to reduce information loss. Furthermore, memristor cross arrays are used for speeding up calculation and reducing system energy consumption for their hardware advantage. The proposed algorithm is evaluated on both static and neuromorphic datasets using fully connected and convolutional network architecture, and the experimental results indicate that our approach has a high performance compared with previous research.展开更多
Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented...Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.展开更多
Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological neurons.However,due to its computational complexity,many SNNs implementations are limited to computer programs.First,...Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological neurons.However,due to its computational complexity,many SNNs implementations are limited to computer programs.First,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals.The synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with hardware.Secondly,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this paper.The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required.The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the circuit.Finally,a small SNN was designed based on the mathematical model of MSSN.After the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.展开更多
Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects teenagers.The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation...Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects teenagers.The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation.Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s back.Currently,detecting the curve of the spine is manually performed,making it a time-consuming task.To overcome this issue,it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS.This research proposes a new integration of ESNN and Feature Extraction(FE)methods and explores the architecture of ESNN for the AIS classification model.This research identifies the optimal Feature Extraction(FE)methods to reduce computational complexity.The ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial.A comparison between the conventional classifier(Support Vector Machine(SVM),Multi-layer Perceptron(MLP)and Random Forest(RF))with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia(HUKM).This dataset consists of various photogrammetry images of the human back with different types ofMalaysian AIS patients to solve the scoliosis problem.The process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale images.This is then followed by feature extraction,normalization,and classification.The experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS classification.This demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.展开更多
Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work pres...Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work presents a new network called SpikeGoogle,which is implemented with GoogLeNet-like inception module.In this inception module,different convolution kernels and max-pooling layer are included to capture deep features across diverse scales.Experiment results on small NMNIST dataset verify the results of the authors’proposed SpikeGoogle,which outperforms the previous Spiking Convolutional Neural Network method by a large margin.展开更多
Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,exces...Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing.展开更多
Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e a...Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e any cyclicity in time,ther efor e pr oducing difficulties in mining temporal features from the data.In this w ork,the third-gener ation neur al netw ork-the spiking neur al netw ork(SNN)-is utilized in bearing fault diagnosis.SNNs incorpor ate tempor al concepts and utilize discrete spike sequences in communication,making them more biolo gically e xplanatory.Inspired by the classic CNN LeNet-5 fr amew ork,a bearing fault diagnosis method based on a convolutional SNN is proposed.In this method,the spiking convolutional network and the spiking classifier network are constructed by using the inte gr ate-and-fire(IF)and leaky-inte gr ate-and-fire(LIF)model,respectively,and end-to-end training is conducted on the overall model using a surrogate gradient method.The signals are adaptively encoded into spikes in the spiking neuron layer.In addition,the network utilizes max-pooling,which is consistent with the spatial-temporal characteristics of SNNs.Combined with the spiking con volutional la y ers,the netw ork fully extracts the spatial-temporal featur es fr om the bearing vibration signals.Experimental validations and comparisons are conducted on bearings.The results show that the proposed method achieves high accuracy and takes fewer time steps.展开更多
Neuromorphic hardware,as a non-Von Neumann architecture,has better energy efficiency and parallelism than the conventional computer.Here,with the numerical modeling spin-orbit torque(SOT)device using current-induced S...Neuromorphic hardware,as a non-Von Neumann architecture,has better energy efficiency and parallelism than the conventional computer.Here,with the numerical modeling spin-orbit torque(SOT)device using current-induced SOT and Joule heating effects,we acquire its magnetization stochastic switching probability as a function of the interval time of input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working.We further demonstrate that the artificial spiking neural network(SNN)built by this SOT device can perform unsupervised handwritten digit recognition with an accuracy of 80%and logic operation learning.Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevices.展开更多
AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by ...AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.展开更多
The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to t...The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed,wide bandwidth,and massive parallelism.Here,we offer a review on the optical neural computing in our research groups at the device and system levels.The photonics neuron and photonics synapse plasticity are presented.In addition,we introduce several optical neural computing architectures and algorithms including photonic spiking neural network,photonic convolutional neural network,photonic matrix computation,photonic reservoir computing,and photonic reinforcement learning.Finally,we summarize the major challenges faced by photonic neuromorphic computing,and propose promising solutions and perspectives.展开更多
This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an...This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an intelligent and feasible non-intrusive load monitoring(NILM)algorithm is urgently needed.However,most recent researches on NILM have not dealt with practical problems when applied to power grid,i.e.,①limited communication for slow-change systems;②requirement of low-cost hardware at the users’side;and③inconvenience to adapt to new households.Therefore,a novel NILM algorithm based on biology-inspired spiking neural network(SNN)has been developed to overcome the existing challenges.To provide intelligence in NILM,the developed SNN features an unsupervised learning rule,i.e.,spike-time dependent plasticity(STDP),which only requires the user to label one instance for each appliance while adapting to a new household.To upgrade the feasibility in NILM,the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor(RC)circuit.In addition,a distributed computing system has been designed that divides the SNN into two parts,i.e.,smart outlets and local servers.Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM,the high-frequency data can be easily compressed as the spike times,and are sent to the local server with limited communication capability,whereas it is unable to handle the traditional NILM.Finally,a series of experiments are conducted using a benchmark public dataset.Meanwhile,the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.展开更多
A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and control...A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.展开更多
McKibben muscles are increasingly used in many robotic applications due to their advantages of lightweight,compliant,and skeletal muscles-like behaviours.However,there are still huge challenges in the motion control o...McKibben muscles are increasingly used in many robotic applications due to their advantages of lightweight,compliant,and skeletal muscles-like behaviours.However,there are still huge challenges in the motion control of McKibben muscles due to the system nonlinearity(e.g.,hysteresis)and model uncertainties.To investigate the control issues,a soft artificial arm actuated by an antagonistic pair of McKibben muscles,mimicking the biological structure of skeleton-muscle systems,is developed.Inspired by the biological motor control capability that humans can control and coordinate a group of muscles to achieve complex motions,a cerebellum-like controller based on Spiking Neural Networks(SNNs)is employed for the motion control of the developed artificial arm.Benefit from the employment of the SNN-based cerebellar model,the proposed control scheme provides online adaptive learning capability,good computational efficiency,fast response,and strong robustness.Finally,several simulations and experiments are conducted subject to different environmental disturbances.Both simulation and experimental results verify that the proposed method can achieve good tracking performance,adaptability,and strong robustness.展开更多
Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic ...Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic pattern.The spiking packets may cause communication pressure on NoC.We propose a path-based multicast routing method to alleviate the pressure.Firstly,all destination nodes of each source node on NoC are divided into several clusters.Secondly,multicast paths in the clusters are created based on the Hamiltonian path algorithm.The proposed routing can reduce the length of path and balance the communication load of each router.Lastly,we design a lightweight microarchitecture of NoC,which involves a customized multicast packet and a routing function.We use six datasets to verify the proposed multicast routing.Compared with unicast routing,the running time of path-based multicast routing achieves 5.1x speedup,and the number of hops and the maximum transmission latency of path-based multicast routing are reduced by 68.9%and 77.4%,respectively.The maximum length of path is reduced by 68.3%and 67.2%compared with the dual-path(DP)and multi-path(MP)multicast routing,respectively.Therefore,the proposed multicast routing has improved performance in terms of average latency and throughput compared with the DP or MP multicast routing.展开更多
Seeing through dense occlusions and reconstructing scene images is an important but challenging task.Traditional framebased image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions du...Seeing through dense occlusions and reconstructing scene images is an important but challenging task.Traditional framebased image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded frames.Event cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal resolution.However,synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream,and the initial brightness is unknown.In this paper,we propose an event-enhanced multi-modal fusion hybrid network for image de-occlusion,which uses event streams to provide complete scene information and frames to provide color and texture information.An event stream encoder based on the spiking neural network(SNN)is proposed to encode and denoise the event stream efficiently.A comparison loss is proposed to generate clearer results.Experimental results on a largescale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.展开更多
The artificial neural network-spiking neural network(ANN-SNN)conversion,as an efficient algorithm for deep SNNs training,promotes the performance of shallow SNNs,and expands the application in various tasks.However,th...The artificial neural network-spiking neural network(ANN-SNN)conversion,as an efficient algorithm for deep SNNs training,promotes the performance of shallow SNNs,and expands the application in various tasks.However,the existing conversion methods still face the problem of large conversion error within low conversion time steps.In this paper,a heuristic symmetric-threshold rectified linear unit(stReLU)activation function for ANNs is proposed,based on the intrinsically different responses between the integrate-and-fire(IF)neurons in SNNs and the activation functions in ANNs.The negative threshold in stReLU can guarantee the conversion of negative activations,and the symmetric thresholds enable positive error to offset negative error between activation value and spike firing rate,thus reducing the conversion error from ANNs to SNNs.The lossless conversion from ANNs with stReLU to SNNs is demonstrated by theoretical formulation.By contrasting stReLU with asymmetric-threshold LeakyReLU and threshold ReLU,the effectiveness of symmetric thresholds is further explored.The results show that ANNs with stReLU can decrease the conversion error and achieve nearly lossless conversion based on the MNIST,Fashion-MNIST,and CIFAR10 datasets,with 6×to 250 speedup compared with other methods.Moreover,the comparison of energy consumption between ANNs and SNNs indicates that this novel conversion algorithm can also significantly reduce energy consumption.展开更多
Artificial synapses and neurons are crucial milestones for neuromorphic computing hardware,and memristors with resistive and threshold switching characteristics are regarded as the most promising candidates for the co...Artificial synapses and neurons are crucial milestones for neuromorphic computing hardware,and memristors with resistive and threshold switching characteristics are regarded as the most promising candidates for the construction of hardware neural networks.However,most of the memristors can only operate in one mode,that is,resistive switching or threshold switching,and distinct memristors are required to construct fully memristive neuromorphic computing hardware,making it more complex for the fabrication and integration of the hardware.Herein,we propose a flexible dual-mode memristor array based on core–shell CsPbBr3@graphdiyne nanocrystals,which features a 100%transition yield,small cycle-to-cycle and device-to-device variability,excellent flexibility,and environmental stability.Based on this dual-mode memristor,homo-material-based fully memristive neuromorphic computing hardware—a power-free artificial nociceptive signal processing system and a spiking neural network—are constructed for the first time.Our dual-mode memristors greatly simplify the fabrication and integration of fully memristive neuromorphic systems.展开更多
Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challeng...Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching(RS)characteristics are different.Here,it is shown that SrFeO_(x)(SFO),an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite(BM)SrFeO_(2).5 phase and conductive perovskite(PV)SrFeO_(3) phase,can be engineered into both neuronal and synaptic devices.Using a BM-SFO single layer as the RS medium,the Au/BM-SFO/SrRuO_(3)(SRO)memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix.By contrast,using a PV-SFO(matrix)/BM-SFO(interfacial layer)bilayer as the RS medium,the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation.Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors,respectively.Using the SFO-based synapses and neurons,fully memristive SNNs are constructed by simulation,which show good performance on unsupervised image recognition.Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs.展开更多
基金financial supports from National Key Research and Development Program of China (2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801904)National Natural Science Foundation of China (No.61974177)+1 种基金National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (62022062)The Fundamental Research Funds for the Central Universities (QTZX23041).
文摘Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.
基金supports from the National Key Research and Development Program of China (Nos.2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801903,2021YFB2801904)the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (No.62022062)+1 种基金the National Natural Science Foundation of China (No.61974177)the Fundamental Research Funds for the Central Universities (No.QTZX23041).
文摘Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.
基金Project supported by the Natural Science Foundation of Chongqing(Grant No.cstc2021jcyj-msxmX0565)the Fundamental Research Funds for the Central Universities(Grant No.SWU021002)the Graduate Research Innovation Project of Chongqing(Grant No.CYS22242)。
文摘In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spatio-temporal information.However, the non-differential spike activity makes SNNs more difficult to train in supervised training. Most existing methods focusing on introducing an approximated derivative to replace it, while they are often based on static surrogate functions. In this paper, we propose a progressive surrogate gradient learning for backpropagation of SNNs, which is able to approximate the step function gradually and to reduce information loss. Furthermore, memristor cross arrays are used for speeding up calculation and reducing system energy consumption for their hardware advantage. The proposed algorithm is evaluated on both static and neuromorphic datasets using fully connected and convolutional network architecture, and the experimental results indicate that our approach has a high performance compared with previous research.
文摘Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.
基金Project supported by the National Key Research and Development Program of China(Grant No.2018 YFB1306600)the National Natural Science Foundation of China(Grant Nos.62076207,62076208,and U20A20227)the Science and Technology Plan Program of Yubei District of Chongqing(Grant No.2021-17)。
文摘Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological neurons.However,due to its computational complexity,many SNNs implementations are limited to computer programs.First,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals.The synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with hardware.Secondly,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this paper.The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required.The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the circuit.Finally,a small SNN was designed based on the mathematical model of MSSN.After the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.
基金This work is supported by the Ministry of Education Malaysia and Universiti Teknologi Malaysia through Research University Grant Scheme(Q.J130000.2651.16J63).
文摘Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects teenagers.The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation.Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s back.Currently,detecting the curve of the spine is manually performed,making it a time-consuming task.To overcome this issue,it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS.This research proposes a new integration of ESNN and Feature Extraction(FE)methods and explores the architecture of ESNN for the AIS classification model.This research identifies the optimal Feature Extraction(FE)methods to reduce computational complexity.The ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial.A comparison between the conventional classifier(Support Vector Machine(SVM),Multi-layer Perceptron(MLP)and Random Forest(RF))with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia(HUKM).This dataset consists of various photogrammetry images of the human back with different types ofMalaysian AIS patients to solve the scoliosis problem.The process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale images.This is then followed by feature extraction,normalization,and classification.The experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS classification.This demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.
基金sponsored by Key‐Area Research and Development Program of Guangdong Province,No.2020B0404020005.
文摘Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work presents a new network called SpikeGoogle,which is implemented with GoogLeNet-like inception module.In this inception module,different convolution kernels and max-pooling layer are included to capture deep features across diverse scales.Experiment results on small NMNIST dataset verify the results of the authors’proposed SpikeGoogle,which outperforms the previous Spiking Convolutional Neural Network method by a large margin.
基金supported by the National Natural Science Foundation of China(Nos.61974164,62074166,62004219,62004220,and 62104256).
文摘Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing.
基金supported by the Natural Science Foundation of China (Grants No.U1934219,62173137 and 52272347)the Hunan Pr ovincial Natur al Science Foundation of China (Grant No.2021JJ50001).
文摘Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e any cyclicity in time,ther efor e pr oducing difficulties in mining temporal features from the data.In this w ork,the third-gener ation neur al netw ork-the spiking neur al netw ork(SNN)-is utilized in bearing fault diagnosis.SNNs incorpor ate tempor al concepts and utilize discrete spike sequences in communication,making them more biolo gically e xplanatory.Inspired by the classic CNN LeNet-5 fr amew ork,a bearing fault diagnosis method based on a convolutional SNN is proposed.In this method,the spiking convolutional network and the spiking classifier network are constructed by using the inte gr ate-and-fire(IF)and leaky-inte gr ate-and-fire(LIF)model,respectively,and end-to-end training is conducted on the overall model using a surrogate gradient method.The signals are adaptively encoded into spikes in the spiking neuron layer.In addition,the network utilizes max-pooling,which is consistent with the spatial-temporal characteristics of SNNs.Combined with the spiking con volutional la y ers,the netw ork fully extracts the spatial-temporal featur es fr om the bearing vibration signals.Experimental validations and comparisons are conducted on bearings.The results show that the proposed method achieves high accuracy and takes fewer time steps.
基金supported by the National Natural Science Foundation of China(Grant No.12074178)the Open Research Fund of Jiangsu Provincial Key Laboratory for Nanotechnology.
文摘Neuromorphic hardware,as a non-Von Neumann architecture,has better energy efficiency and parallelism than the conventional computer.Here,with the numerical modeling spin-orbit torque(SOT)device using current-induced SOT and Joule heating effects,we acquire its magnetization stochastic switching probability as a function of the interval time of input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working.We further demonstrate that the artificial spiking neural network(SNN)built by this SOT device can perform unsupervised handwritten digit recognition with an accuracy of 80%and logic operation learning.Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevices.
基金Project supported in part by the National Key Research and Development Program of China(Grant No.2021YFA0716400)the National Natural Science Foundation of China(Grant Nos.62225405,62150027,61974080,61991443,61975093,61927811,61875104,62175126,and 62235011)+2 种基金the Ministry of Science and Technology of China(Grant Nos.2021ZD0109900 and 2021ZD0109903)the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving ElectronicsTsinghua University Initiative Scientific Research Program.
文摘AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.
基金This work was supported in part by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(62022062)the National Natural Science Foundation of China(61974177,61674119)the Fundamental Research Funds for the Central Universities.
文摘The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed,wide bandwidth,and massive parallelism.Here,we offer a review on the optical neural computing in our research groups at the device and system levels.The photonics neuron and photonics synapse plasticity are presented.In addition,we introduce several optical neural computing architectures and algorithms including photonic spiking neural network,photonic convolutional neural network,photonic matrix computation,photonic reservoir computing,and photonic reinforcement learning.Finally,we summarize the major challenges faced by photonic neuromorphic computing,and propose promising solutions and perspectives.
基金supported by the SGCC Science and Technology Program under project“Distributed High-Speed Frequency Control Under UHVDC Bipolar Blocking Fault Scenario”(No.SGGR0000DLJS1800934)。
文摘This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an intelligent and feasible non-intrusive load monitoring(NILM)algorithm is urgently needed.However,most recent researches on NILM have not dealt with practical problems when applied to power grid,i.e.,①limited communication for slow-change systems;②requirement of low-cost hardware at the users’side;and③inconvenience to adapt to new households.Therefore,a novel NILM algorithm based on biology-inspired spiking neural network(SNN)has been developed to overcome the existing challenges.To provide intelligence in NILM,the developed SNN features an unsupervised learning rule,i.e.,spike-time dependent plasticity(STDP),which only requires the user to label one instance for each appliance while adapting to a new household.To upgrade the feasibility in NILM,the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor(RC)circuit.In addition,a distributed computing system has been designed that divides the SNN into two parts,i.e.,smart outlets and local servers.Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM,the high-frequency data can be easily compressed as the spike times,and are sent to the local server with limited communication capability,whereas it is unable to handle the traditional NILM.Finally,a series of experiments are conducted using a benchmark public dataset.Meanwhile,the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.
基金supported by the National Basic Research Program of China(2015CB351806)the National Natural Science Foundation of China(61806011,61825101,61425025,and U1611461)+4 种基金the National Postdoctoral Program for Innovative Talents(BX20180005)the China Postdoctoral Science Foundation(2018M630036)the International Talent Exchange Program of Beijing Municipal Commission of Science and Technology(Z181100001018026)the Zhejiang Lab(2019KC0AB03 and 2019KC0AD02)the Royal Society Newton Advanced Fellowship(NAF-R1-191082).
文摘A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.
文摘McKibben muscles are increasingly used in many robotic applications due to their advantages of lightweight,compliant,and skeletal muscles-like behaviours.However,there are still huge challenges in the motion control of McKibben muscles due to the system nonlinearity(e.g.,hysteresis)and model uncertainties.To investigate the control issues,a soft artificial arm actuated by an antagonistic pair of McKibben muscles,mimicking the biological structure of skeleton-muscle systems,is developed.Inspired by the biological motor control capability that humans can control and coordinate a group of muscles to achieve complex motions,a cerebellum-like controller based on Spiking Neural Networks(SNNs)is employed for the motion control of the developed artificial arm.Benefit from the employment of the SNN-based cerebellar model,the proposed control scheme provides online adaptive learning capability,good computational efficiency,fast response,and strong robustness.Finally,several simulations and experiments are conducted subject to different environmental disturbances.Both simulation and experimental results verify that the proposed method can achieve good tracking performance,adaptability,and strong robustness.
基金supported by the National Key Research and Development Program of China under Grant Nos.2018YFB2202-603and2020AAA0104602.
文摘Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic pattern.The spiking packets may cause communication pressure on NoC.We propose a path-based multicast routing method to alleviate the pressure.Firstly,all destination nodes of each source node on NoC are divided into several clusters.Secondly,multicast paths in the clusters are created based on the Hamiltonian path algorithm.The proposed routing can reduce the length of path and balance the communication load of each router.Lastly,we design a lightweight microarchitecture of NoC,which involves a customized multicast packet and a routing function.We use six datasets to verify the proposed multicast routing.Compared with unicast routing,the running time of path-based multicast routing achieves 5.1x speedup,and the number of hops and the maximum transmission latency of path-based multicast routing are reduced by 68.9%and 77.4%,respectively.The maximum length of path is reduced by 68.3%and 67.2%compared with the dual-path(DP)and multi-path(MP)multicast routing,respectively.Therefore,the proposed multicast routing has improved performance in terms of average latency and throughput compared with the DP or MP multicast routing.
基金supported by National Natural Science Funds of China (Nos. 62088102 and 62021002)Beijing Natural Science Foundation, China (No. 4222025)
文摘Seeing through dense occlusions and reconstructing scene images is an important but challenging task.Traditional framebased image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded frames.Event cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal resolution.However,synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream,and the initial brightness is unknown.In this paper,we propose an event-enhanced multi-modal fusion hybrid network for image de-occlusion,which uses event streams to provide complete scene information and frames to provide color and texture information.An event stream encoder based on the spiking neural network(SNN)is proposed to encode and denoise the event stream efficiently.A comparison loss is proposed to generate clearer results.Experimental results on a largescale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.
基金the National Key Research and Development Program of China(No.2020AAA0105900)National Natural Science Foundation of China(No.62236007)Zhejiang Lab,China(No.2021KC0AC01).
文摘The artificial neural network-spiking neural network(ANN-SNN)conversion,as an efficient algorithm for deep SNNs training,promotes the performance of shallow SNNs,and expands the application in various tasks.However,the existing conversion methods still face the problem of large conversion error within low conversion time steps.In this paper,a heuristic symmetric-threshold rectified linear unit(stReLU)activation function for ANNs is proposed,based on the intrinsically different responses between the integrate-and-fire(IF)neurons in SNNs and the activation functions in ANNs.The negative threshold in stReLU can guarantee the conversion of negative activations,and the symmetric thresholds enable positive error to offset negative error between activation value and spike firing rate,thus reducing the conversion error from ANNs to SNNs.The lossless conversion from ANNs with stReLU to SNNs is demonstrated by theoretical formulation.By contrasting stReLU with asymmetric-threshold LeakyReLU and threshold ReLU,the effectiveness of symmetric thresholds is further explored.The results show that ANNs with stReLU can decrease the conversion error and achieve nearly lossless conversion based on the MNIST,Fashion-MNIST,and CIFAR10 datasets,with 6×to 250 speedup compared with other methods.Moreover,the comparison of energy consumption between ANNs and SNNs indicates that this novel conversion algorithm can also significantly reduce energy consumption.
基金Natural Science Foundation of Tianjin City,Grant/Award Number:19JCYBJC17300National Natural Science Foundation of China,Grant/Award Numbers:21790052,51802220。
文摘Artificial synapses and neurons are crucial milestones for neuromorphic computing hardware,and memristors with resistive and threshold switching characteristics are regarded as the most promising candidates for the construction of hardware neural networks.However,most of the memristors can only operate in one mode,that is,resistive switching or threshold switching,and distinct memristors are required to construct fully memristive neuromorphic computing hardware,making it more complex for the fabrication and integration of the hardware.Herein,we propose a flexible dual-mode memristor array based on core–shell CsPbBr3@graphdiyne nanocrystals,which features a 100%transition yield,small cycle-to-cycle and device-to-device variability,excellent flexibility,and environmental stability.Based on this dual-mode memristor,homo-material-based fully memristive neuromorphic computing hardware—a power-free artificial nociceptive signal processing system and a spiking neural network—are constructed for the first time.Our dual-mode memristors greatly simplify the fabrication and integration of fully memristive neuromorphic systems.
基金The authors would like to thank the National Natural Science Foundation of China(Nos.92163210,U1932125,52172143)Science and Technology Program of Guangzhou(No.2019050001)Natural Science Foundation of Guangdong Province(No.2020A1515010996).
文摘Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching(RS)characteristics are different.Here,it is shown that SrFeO_(x)(SFO),an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite(BM)SrFeO_(2).5 phase and conductive perovskite(PV)SrFeO_(3) phase,can be engineered into both neuronal and synaptic devices.Using a BM-SFO single layer as the RS medium,the Au/BM-SFO/SrRuO_(3)(SRO)memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix.By contrast,using a PV-SFO(matrix)/BM-SFO(interfacial layer)bilayer as the RS medium,the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation.Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors,respectively.Using the SFO-based synapses and neurons,fully memristive SNNs are constructed by simulation,which show good performance on unsupervised image recognition.Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs.
