A memristor based unsupervised neuromorphic system towards fast and energyef. The human brains neocortex covers the brains surficial area, which is known to carry out the most intelligence functions. Memristor crossbarbased neuromorphic computing system. A novel processinginmemory architecture for neural network computation in rerambased main memory ping chi. Stdpbased unsupervised learning has been proposed mainly for binary synapses,69. Training methods have been proposed to program the memristors in a crossbar by following existing training algorithms in neural network models.
A novel processinginmemory architecture for neural. Unsupervised clustering before training after training wisconsin iris wine 2 5 3 5 4 5 5 5 6 5 7 51 n t1 50 1 5 0 5 1 n t 0. A reconfigurable low power high throughput architecture. Taha, memristor crossbar based unsupervised training, in the ieee national aerospace and electronics conference, 2015. Due to the limited precision of the memristor, we divide the multiple bits of computation into many groups. Each memristor in the crossbar is trained by a stdp learning rule.
In this work, the memristor crossbar based ncs is trained to implement a neural network for classification tasks. Analogue signal and image processing with large memristor. Recently, various hardware implementations of cenns have emerged in the literature, with field programmable gate array fpga being one of the most popular choices due to its high flexibility and low timetomarket. A programmable ultraefficient memristorbased accelerator for machine learning inference. Training process tunes the connection weights memristor resistances of a neural. In this dissertation, we quantitatively analyzed the robustness of memristor crossbar based ncs when considering the device process variations, signal fluctuation and irdrop. Besides sparse learning, memristor crossbars have also been considered for implementing and training a probabilistic graphical model and image learning 32, 33. Cellular neural networks cenns have been widely adopted in image processing tasks. Abstract this letter presents a new memristor crossbar array system and demonstrates its applications in image learning. Hossein mohammad khanlou, ali sadollah, bee chin ang, joonghoon kim, sepehr talebian, azadeh ghadimi. Learning in energyefficient neuromorphic computing algorithm and architecture codesign this edition first published. Pdf memristor crossbarbased unsupervised image learning. It is successfully inspired by the human brain which is capable of solving many problems in the areas of data storage, patternrecognition, image processing and optimization problem.
Deconvolution and convolution operations in these circuits are performed using memristive crossbar based dot product multiplication. Memristor devices are considered to have the potential to implement unsupervised learning, especially spike timingdependent plasticity stdp, in the field of neuromorphic hardware research. Towards memristor based accelerator for sparse matrix. Index termscnn, dnn, memristor, analog, neural, acceler ator. Neural network nn and deep learning dl have the potential to provide optimal solutions in various applications including image speech recognition and natural language processing, and are gaining a lot of attention recently. Crossbar arrays based on twoterminal resistive switches have been proposed as a leading candidate for future memory and logic applications. The thickness of neocortex has been observed as thin as 2. Memristor crossbarbased unsupervised image learning semantic. Hierarchical temporal memory using memristor networks. Asymmetrical training scheme of binarymemristorcrossbarbased neural. With the advent of edge computing, htm can be a potential algorithm to implement onchip near sensor data. Materials free fulltext hybrid circuit of memristor and. In this paper, we present an implementation scheme of memristorbased multilayer feedforward smallworld neural network mfsnn inspirited by the lack of the hardware realization of the mfsnn on account of the need of a large number of electronic neurons and synapses.
Although memristorinspired ai applications are different, the common underlying theme is the design of a mathematical programming solver for an optimization problem specified by a. Onchip learning circuits for supervised, unsupervised, and deep learning. Tswj the scientific world journal 1537744x 23566140 hindawi publishing corporation 10. A lowcomputationcomplexity, energyefficient, and high. Deep networks are stateoftheart models used for understanding the content of images, videos, audio.
Efficient hardware implementation of cellular neural. A novel memristive multilayer feedforward smallworld neural. A convolutional neural network accelerator with insitu analog arithmetic in crossbars ali sha. And the memristor crossbarbased computing engine mce can be used to realize a low power approximate computing system with 400 gflopsw 6. Hasan r and taha t m 2016 memristor crossbar based unsupervised training proc. After five years in industry, he joined university of pittsburgh in 2010 as assistant professor and then promoted to associate professor with tenure in 2014, held bicentennial alumni faculty fellow. More specially, a mathematical closedform chargegoverned memristor model is presented with derivation procedures. Different programming circuits with time slot techniques are designed for the mca consisting of the nonlinear hp memristor hpmca and the mca composed of the piecewise linear threshold memristor tmca. Since w is predetermined based on software training and. Memristor crossbar based neuromorphic computing system. Some impressive work have been done to realize basic supervised 8, 6 or unsupervised learning schemes 15 on crossbar to handle.
Onchip training of memristor crossbar based multilayer neural. Neuromorphic computation based on memristor is an advanced approach for information processing system, and it offers both high density and high connectivity. The crossbar not only stores the synaptic weights, it also. Jan 26, 2017 since the selflearning circuit and the recognition circuit are based on memristor device, selection of memristor model is a noteworthy issue in the proposed neuromorphic system. The computation of each group can achieve adequate accuracy by memristor crossbarbased structure with limitedprecision of memristors. Oct 18, 2017 besides sparse learning, memristor crossbars have also been considered for implementing and training a probabilistic graphical model and image learning 32, 33. Assistant professor of electrical and computer engineering. In this work, the memristor crossbarbased ncs is trained to implement a neural network for classification tasks. Taha, memristor crossbar based unsupervised training, in. In this paper, we present an implementation scheme of memristor based multilayer feedforward smallworld neural network mfsnn inspirited by the lack of the hardware realization of the mfsnn on account of the need of a large number of electronic neurons and synapses. Unsupervised electric motor fault detection by using deep autoencoders. For memristor crossbar based framework, the performance is measured as the time memristor modeling and case study in neuromorphic computing, spent on memristor programming, crossbar results evaluation and partial results merging.
Recent progress on fabrication of memristor and transistorbased neuromorphic devices for high signal processing speed with low power consumption. The pis will develop and characterize the memristors and then design deep learning circuits for training based on the characterized memristor devices. More specially, a mathematical closedform chargegoverned memristor model is presented with derivation procedures and the. Memristor crossbar deep network implementation based on a. The stateoftheart nn and dl algorithms, such as multilayer perceptron mlp and convolutional neural network. Pdf memristor based autoencoder for unsupervised real. Neuromorphic computation based on memristor is an advanced approach. Such a property leads to the state drifting in a slow rate when using the memristor.
The output is a single pixel used in part to construct the edge detected output image. To address these limitations, we present puma, a programmable ultraefficient memristor based accelerator. We are carrying out detailed analysis through accurate circuit level simulations of several types of memristor crossbar based circuits. In this work, we propose a lowcomplexity and ultraefficient memristorbased cs sol ver. The computational merit of memristor crossbars has been stressed in various applications such as linear sol vers 12, dictionary learning, autoencoder 14, and unsupervised learning 15.
They provide excellent classification ability at the cost of a more complex data manipulation process. Similar to dadiannao, the system is organized into multiple nodestiles, with memristor crossbar arrays forming the heart of each tile. However, software also continues to improve with central processing unit. Ruizhe cai, ao ren, yanzhi wang, sucheta soundarajan, qinru qiu. Towards memristor based accelerator for sparse matrix vector. A lowcomputationcomplexity, energyefficient, and highperformance linear program solver using memristor crossbars.
The binary memristors which are based on filamentaryswitching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process. This letter uses image overlay technique on memristor crossbar array mca structure for image storing. Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks. In this study, a neuromorphic hardware system for multilayer unsupervised learning was designed, and unsupervised learning was performed with a memristor neural network. Machine learning algorithms have recently grown in promi. A memristorbased optimization framework for ai applications. Examples of interest include retinal implants for vision restoration and onth. A memristor based unsupervised neuromorphic system towards. Memristor crossbarbased unsupervised image learning article pdf available in neural computing and applications 252 august 2014 with 121 reads how we measure reads. In this paper, we propose a memristor crossbar based computing scheme based on the result of 8.
Puma is a spatial architecture designed to preserve the storage density of memristor crossbars to enable mapping ml applications using onchip memory only. A memristor crossbarbased computation scheme with high precision. Inline calibration for memristor crossbarbased computing engine boxun li 1, yu wang, yiran chen 2, hai helen li, huazhong yang1 1dept. When the current flows into the memristive device from the positive pole to the negative pole, one can get the relationship curve the blue line between memristance and charge through it as. Onchip training of memristor crossbar based multilayer. A functional hybrid memristor crossbararraycmos system for. In this paper, we propose a memristor crossbarbased computing scheme based on the result of 8. Eda challenges for memristorcrossbar based neuro morphic. Recent advances in development of memristor devices and crossbar integration allow us to implement a lowpower onchip neuromorphic computing system ncs with small footprint.
The software or microprocessor based pretraining up to certain. Then, a simple memristor crossbarbased analog circuit is designed to implement this neurofuzzy system which offers very interesting properties. Neuromorphic crossbar circuit with nanoscale filamentary. Based on our analysis, we will explore deep understanding on hardware training methods, e. Here we demonstrate a highdensity, fully operational hybrid crossbarcmos system composed of a transistor and diodeless memristor crossbar array vertically integrated on top of a cmos chip by taking advantage of the intrinsic nonlinear characteristics. Similar to a network model in machine learning theory, ncs is trained and tested by two separate groups of data samples. The energy is measured as total energy dissipation on controller, data converters, opamps and. School of computing, university of utah, salt lake city, utah, usa email. In this system, the memristor model with forgetting effect is chosen as element in all crossbar arrays with infinite value of. The computation of each group can achieve adequate accuracy by memristor crossbar based structure with limitedprecision of memristors.
Realizing spiketiming dependent plasticity learning rule in. Recent progress on fabrication of memristor and transistor. By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energyefficient platform. A convolutional neural network accelerator with in. This paper presents a simulated memristor crossbar implementation of a deep convolutional neural network cnn. It was proposed theoretically by chua 14 in 1971 and demonstrated for the first time by hewlettpackard hp laboratory researchers while developing crossbar based ultrahighdensity nonvolatile memories in 2008 using a tio 2 thin film as an ionic conductive material and sandwiched by pt electrodes. We first summarize cnns targeted at image detection and. In the past few years deep neural networks implemented on gpu clusters have become the state of the art in image classification. In this paper, a neuromorphic crossbar circuit with binary memristors is proposed for speech recognition. This letter presents a new memristor crossbar array system and demonstrates its applications in image learning. It is noteworthy that the memristor is a twoterminal element with polarity, which is shown in figure 3a.
In memristor technology, the current through a device in history is re. Realtime imageprocessing applications impose severe design constraints in terms of area and power. A memristor based unsupervised neuromorphic system. We exploited a hardware and software codesign approach to map the computation blocks in gan to the memristorbased crossbars efficiently. Static, statistical, and stochastic methodologies, a book chapter in cmos and postcmos perspectives of electronic device scaling, edited by saraju mohanty, springer, 2015. The software autoencod e r shows lower threshold values when. Here we demonstrate a highdensity, fully operational hybrid crossbar cmos system composed of a transistor and diodeless memristor crossbar array vertically integrated on top of a cmos chip by taking advantage of the intrinsic nonlinear characteristics. A functional hybrid memristor crossbararraycmos system. Neuromorphic system design and application by beiye liu b. When a software network layer is too large to fit into a core. Thus, we develop a neuromorphic crossbar circuit using.
Then, a simple memristor crossbar based analog circuit is designed to implement this neurofuzzy system which offers very interesting properties. Giving a sine stimulus to the memristor, we get the simulation results using matlab software. Laboratory researchers while developing crossbarbased ultrahighdensity nonvolatile memories in 2008 using a tio 2 thin film as an. The controlled pulse and image overlay technique are introduced for the programming of memristor crossbars and promising a better performance for noise reduction. The timeslot technique is helpful for improving the processing speed of image. In this work, we developed a memristorbased unsupervised neuromorphic system for a fast and energyefficient gan computation to solve the above challenges. Materials free fulltext hybrid circuit of memristor. Memristor crossbars are circuits capable of performing ana log matrixvector.
Department of electrical and computer engineering, university of california, santa barbara, ca 93106, usa hp labs, palo alto, ca 94304, usa. A memristor is formed at each crosspoint, and can be programmed to different conductance states represented in greyscale by controlling the internal ion redistribution inset. Bibliographic content of neural computing and applications, volume 25. Memristor crossbar array for image storing springerlink.
In this work, we propose a lowcomplexity and ultraefficient memristor based cs sol ver. The schematic of the selflearning crossbar circuit is shown in the top of. The training algorithm for the memristor crossbar based. There are two memristor crossbars in this circuit, each representing a layer of neurons. Thus, neuromorphic systems based on memristor crossbars have. Memristor based autoencoder for unsupervised realtime network intrusion and. Memristor, cognitive computing hardware, low power computing. Realizing spiketiming dependent plasticity learning rule.
Forgetting memristor based neuromorphic system for pattern. Image processing with large memristive crossbars is demonstrated in 24. Mazumder, learning in memristor crossbarbased spiking neural networks through modulation of weight dependent spiketimingdependent plasticity, ieee trans. An example of generated images after training this network on mnist. A novel memristive multilayer feedforward smallworld. The dsd is flexible to realize synaptic connections but its area cost increases exponentially with the neuron number because of routing congestion. Memristor crossbarbased unsupervised image learning. In this article, the status of memristorbased neuromorphic computation. Unsupervised training or deep network training is not examined in these. Mazumder, learning in memristor crossbarbased spiking neural networks through modulation of weight dependent spiketimingdependent plasticity, ieee.
Memristor crossbar based low power computing tarek m. Digital cmos verified via fpga implementation memristor crossbar both learning and recognition specialized versions for. Pdf memristor based autoencoder for unsupervised realtime. Memristor crossbars are circuits capable of performing analog matrixvector multiplications, overcoming the fundamental energy. A memristor crossbarbased computation scheme with high. In this paper a novel neurofuzzysystem is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Palmer, spice analysis of dense memristor crossbars for low power neuromorphic processor designs ieee national aerospace and electronics conference, june, 2015. The six neocortical layers seem to be columnar, in which the complicated vertical and horizontal synaptic connections are intertwined among neurons to form. Although memristor inspired ai applications are different, the common underlying theme is the design of a mathematical programming solver for an optimization problem specified by a. Chen, patents relevant to spintronic memristor, recent patents on electrical engineering, vol. The novel computing systems to be developed will employ memristor circuits to accelerate the training phase of deep learning algorithms. This paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, hierarchical temporal memory htm.
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