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... brain can perform complex tasks such as unstructured data classification and image recognition. In human brain, excitatory and inhibitory postsynaptic potentials are delivered from presynaptic neuron to postsynaptic neuron through chemical and electrical signal at synapses, driving the change of synaptic weight, as shown in Figure 2. The synaptic weight is precisely adjusted by the ionic flow through the neurons. ...
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... 11 is the proposed spike shape, which is similar to the biological spikes. Figure 12 shows the STDP curves produced by the proposed spike shape. In Figure 12, the vertical axis shows the average Figure 11. ...
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... 12 shows the STDP curves produced by the proposed spike shape. In Figure 12, the vertical axis shows the average Figure 11. Proposed spike shape used for processing and learning purposed [17]. ...
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... the different number and size of input and output single, the structure of the second layer is distinctly different from the one. The circuit design of the second convolution layer is shown in Figure 20. Each column represents six different feature map convolution processes, and will be operated with 12 different kernels in parallel methods. ...
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... circuit used to complete this operation can be seen in Figure 21. The memristor crossbar used in classification layer is to store a weight matrix, which is different with storing a set of convolu- tion kernels arrays in convolution circuits. ...
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... CNN algorithm purely in simulation under these training conditions results in 92% classification accuracy as shown in Figure 22. And, the simulation process is to test the accuracy of the memristor based CNN recognition system described in the previous section. ...
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... neural networks, or RNNs, are the main tool for handling sequential data, which involve variable length inputs or outputs [40]. Compared with multilayer network, the weights in an RNN are shared across different instances of the artificial neurons, each associated with Figure 20. The circuit that is used to implement the second convolution layer. ...
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... simplest architecture of RNNs is illustrated in Figure 23 [40]. The left of Figure 24 shows the ordinary recurrent network circuit with weight matrices U, V, W denoting three different kind of connection (input-to-hidden, hidden-to-output, and hidden-to-hidden, respectively). ...
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... simplest architecture of RNNs is illustrated in Figure 23 [40]. The left of Figure 24 shows the ordinary recurrent network circuit with weight matrices U, V, W denoting three different kind of connection (input-to-hidden, hidden-to-output, and hidden-to-hidden, respectively). Each circle indicates a whole vector of activations. ...
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... circle indicates a whole vector of activations. The right of Figure 24 is a time-unfolded flow graph, where each node is now associated with one particular time instance. ...
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... this case, a nine synapses Hopfield network is realized with six memristors and three neurons. As shown in Figure 25, the artificial neuron has three inputs and each input, Ni ¼ (i ¼ 1, 2, and 3), is connected to a synapse with synaptic weight of w i . The output of the three-input binary artificial neuron is expressed as ...
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... artificial neuron was constructed, as shown in Figure 26. An operational amplifier is used to sum the inputs. ...
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... synaptic weights corresponding to input N 1 , N 2 , and N 3 are w 1 ¼ AE M1 M1þR , W 2 ¼ AE M2 M2þR and W 3 ¼ AE M3 M3þR , respectively (M 1 , M 2 , and M 3 are the resistance of the memristors, respectively, and the resistance of R is fixed at 3 MΩ). In the circuit shown in Figure 26, transmission gates B 1 , B 2 , and B 3 reform signals without modifying its polarity, inverters I 1 , I 2 and I 3 generate negative synaptic weights. ...
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... architecture of a 3-bit MHN implemented with nine synapses is shown in Figure 27. The synaptic weight from neuron i to neuron j is denoted as w i, j , which is mapped to resistance of the corresponding memristor M i, j ,. ...
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... synaptic weight from neuron i to neuron j is denoted as w i, j , which is mapped to resistance of the corresponding memristor M i, j ,. M i, j , and w i, j are represented by the resistance matrix, respectively Figure 28, and all the demonstration below is based on this circuit. The threshold vector T ¼ (θ 1 , θ 2 , θ 3 ) represents the threshold of the artificial neurons (neurons 1, 2, and 3), and the state vector X ¼ (X 1 , X 2, X 3 ) represents the states of the three neurons, respectively. ...
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Introduction
Synapse based on two successive memristors builds the synaptic weights of the artificial neural network for training three-bit parity problem and five-character recognition.
Methods
The proposed memristor synapse circuit creates positive weights in the range [0;1], and maps it to range [-1;1] to program both the positive and negative...
Citations
... The memristor device [1,2] has successfully supplemented the modern integrated circuitry. Due to the memory property, the memristor is well suited for various information storage and processing systems, including the construction of neuromorphic systems [3][4][5][6]. Due to the inertia property of memristors, it is possible to design memristor oscillators of various types [7][8][9][10]. ...
... The capability of memristor analog memory can be successfully used in artificial neurons (AN) and AN-based networks (ANN) [5,6,17] because they primarily meet the requirements for neuronal connections [18,19]. It can be mentioned that the memristor properties make it possible to create memristor-based AN. ...
This paper discusses the application of coupled reactance-less memristor-based oscillators (MBO) with binary output signals in oscillatory networks. A class of binary-coupled memristor oscillators provides simple integration with standard CMOS logic elements. Сombining MBOs with binary logic elements ensures the operation of complex information processing algorithms. The analysis of the simplest networks based on MBOs is performed. The typical reactance-less MBO with current and potential inputs is considered. The output responses for input control signals are analyzed. It is shown that the current input signal impacts primarily the rate of memristor resistance variation, while the potential input signal changes the thresholds. The exploit of the potential input for the synchronization of coupled MBOs and current control input in order to provide the necessary encoding of information is suggested. The example of the application of coupled MBOs in oscillatory networks is given, and results of simulation are presented.
... Focusing on specific implementations that use memristors based on the crossbar array structure, it is worth noting first that this is one common approach found in the literature [7]. By using this structure, vector-matrix multiplications, which are a fundamental building block in all types of neural networks, are efficiently implemented by following an analog approach (i.e., by adding current flows). ...
Neuromorphic systems based on hardware neural networks (HNNs) are expected to be an energy and time-efficient computing architecture for solving complex tasks. In this paper, we consider the implementation of deep neural networks (DNNs) using crossbar arrays of memristors. More specifically, we considered the case where such devices can be configured in just two states: the low-resistance state (LRS) and the high-resistance state (HRS). HNNs suffer from several non-idealities that need to be solved when mapping our software-based models. A clear example in memristor-based neural networks is conductance variability, which is inherent to resistive switching devices, so achieving good performance in an HNN largely depends on the development of reliable weight storage or, alternatively, mitigation techniques against weight uncertainty. In this manuscript, we provide guidelines for a system-level designer where we take into account several issues related to the set-up of the HNN, such as what the appropriate conductance value in the LRS is or the adaptive conversion of current outputs at one stage to input voltages for the next stage. A second contribution is the training of the system, which is performed via offline learning, and considering the hardware imperfections, which in this case are conductance fluctuations. Finally, the resulting inference system is tested in two well-known databases from MNIST, showing that is competitive in terms of classification performance against the software-based counterpart. Additional advice and insights on system tuning and expected performance are given throughout the paper.
... [73][74][75] Within that framework, emulsions have been shown to exhibit similar mechanical properties and have for this reason been used to measure cellular forces both in vitro 76 and in vivo. 77,78 This reductionist approach could thus shed light on behavioral transitions in developing tissues upon adhesion modulation and will be the focus of future investigations. ...
... The axon of one neuron is connected to a dendrite of another neuron through a synapse, enabling the transmission of electrical signals (in the form of action potentials) and forming a complex neural circuit. Adapted from[77].Each neural circuit maintains its own function and ensures the detection, transmission and processing of the information. Neural circuits are the basis of neural functions in health and disease, and the faulty assembly or disintegration of these circuits can result in disorders of the nervous system. ...
In my PhD work, I used tissue-mimetic systems to understand the physical basis of collective remodeling in biological tissues. In particular, I focused on the interplay between adhesion and mechanical forces and how it controls the emergence of tissue architecture during morphogenesis. Indeed, during morphogenesis, a cell aggregate is subjected to large successive elongations and folds that give rise to the highly organized 3D structures found in the embryo.To study the mechanical pathways of tissue remodeling, we used both an in vitro bottom-up approach, using a minimal set of ingredients to reproduce biomimetic tissues, and an in vivo study aiming at measuring forces inside developing tissues. In both cases, we used biomimetic emulsions that were shown to reproduce the minimal mechanical and adhesive properties of cells in biological tissues. These emulsions are stabilized with phospholipids and can be functionalized with binders to induce specific interactions between the droplets. The first aspect of my project was to study the elasto-plastic behavior of adhesive emulsions under mechanical perturbations, by flowing them in microfluidic constrictions with controlled geometries. Adhesion between the droplets was introduced either through non-specific depletion forces, or through specific binding between biomimetic droplets. Image analysis allowed us to distinguish between an elastic response, in which the droplets deformed and kept their neighbors, and a plastic response, in which droplets rearranged their positions irreversibly. We found that while the presence of adhesion does not affect the global topology of rearrangements of the droplets, it slows down the local dynamics of individual rearrangements. As a result, droplets exhibit larger deformations and are globally aligned with the direction of tissue elongation. That could be the signature of an adhesion-induced polarization process in elongating tissues. The second aspect of my project was done in collaboration with Marie Breau (LBD-IBPS). It consisted in using oil droplets as force sensors in developing zebrafish embryos. In particular, the injection of biocompatible oil droplets in their olfactory placode allowed us to measure the presence of anteroposterior compressive forces that can contribute to axone elongation of the olfactory neurons. Further studies will be conducted in order to obtain the full force map in the placode and decipher the origin of the forces driving axonal growth. These complementary approaches both paved the way to a better understanding of the role of forces and adhesion during morphogenesis.
... Biological neuron[41] ...
The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.
... Attracted by the fascinating potential of AI computing paradigm, great efforts are enforced to develop various methods and algorithms for enabling artificial neural networks. During the past decade, leveraging memristive crossbar arrays (MCAs) for this purpose was proposed/ reviewed by several dedicated review papers [67][68][69]. The advantages of artificial neural networks implemented by MCAs in comparison to their counterparts based on the traditional CMOS technology, e.g., CMOS-based field-programmable gate array (FPGA) [70], can be summarized as follows: 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 Reconfigurability of MCAs: A memristive cell is capable of realizing functions that need several transistors in a CMOS circuit [71] due to its intrinsic reconfigurability. ...
Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.
... On this research front, new ways to engineer and optimize memristors are being proposed [11]. Apart from the advantages of using these devices for computation [12] (such as energy efficiency [13], compared to transistor-based computers), memristors can be also used in machine learning schemes [14,15]. The relevance of the memristor lies in its ubiquitous presence in models which describe natural processes, especially those involving biological systems. ...
A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.
... Мемристоры считаются перспективными для применения в устройствах энергонезависимой компьютерной памяти нового поколения [5], нейроморфных компьютерах [6] и др. Однако в настоящее время практическое применение мемристоров ограничено нестабильностью параметров РП [7], фундаментальной причиной которого является стохастическая природа разрушения и восста-новления филаментов в функциональном диэлектрическом слое. ...
Local resistive switching in the contact of an atomic force microscope (AFM) probe to ZrO2(Y) films (including those with a Ta2O5 sublayer) on conducting substrates was studied. Switching was performed by triangular voltage pulses with the imposition of a high-frequency sinusoidal signal. The dependence of the difference in the current strength through the AFM probe in the low-and high-resistance States of dielectric films on the frequency of the high-frequency sinusoidal signal was observed at frequencies corresponding to the characteristic frequency of jumps of O2 ions - for oxygen vacancies in ZrO2 (Y) and Ta2O5 at 300K. The effect is associated with resonant activation of O2 ion migration along with oxygen vacancies by an external high-frequency electric field.
... The model continues with the ideas of McCulloch, Pitts, and Hebb and uses concepts similar to a neuro memristive system [4]. The model also uses connections that alter their state (discretely), in a manner similar to a memristor [5,6]. ...
... • As with the customized memristor neural network designs presented by Anping Huang et al. [6], the DACL can replicate a wide range of neural networks by changing its configuration without the cost of new hardware. [14] for a combination of network designs. ...
... • The creation of DACL network templates (see section6) with inbuilt feedback can allow structured mapping onto deep learning networks (Battaglia et. al, 'Relational Inductive Biases, Deep Learning and Graph Networks') ...
... Memristor can be referred as a passive nano-scale two terminal element with variable resistance. It has numerous advantages over semiconductors and may pave the future of electronics due to following properties: non volatile resistive states(ON/OFF) [12] that results in low power consumption [18], [20] nano-scale size that triggers high switching speed [18], [21] which is especially essential in logic circuits [1]. ...
... Memristor can be referred as a passive nano-scale two terminal element with variable resistance. It has numerous advantages over semiconductors and may pave the future of electronics due to following properties: non volatile resistive states(ON/OFF) [12] that results in low power consumption [18], [20] nano-scale size that triggers high switching speed [18], [21] which is especially essential in logic circuits [1]. ...
... However crossbar arrays are vulnerable to sneak currents, parasitic resistances and voltage limitations what makes it unsuitable for placing lots of synapses to compensate area overhead [19], [20]. Moreover the one essential benefit of memristor is the possibility to be compatible with CMOS [10], [11], [18]. In [14] this property is used in logic computations in a form of MeMOS(Memristor-CMOS) hybrid logic integration. ...
Abstract—Sigmoid and hyperbolic tangent functions are the
computational elements of neural networks and applied very
widely. This paper is aimed to propose a simple design for
improving the tanh-like passive resistive-type neuron by intro-
ducing memristor. Minimal leakage current and area, low power
consumption and non-volatile memory - these features make
the memristor promising and powerful tool in circuit design.
However since memristive devices are not capable to supply
energy to a circuit, they should be coupled with conventional
CMOS devices thus forming hybrid circuit configurations. In
the frame of this study already proposed circuit for passive
neuron is examined: elements are replaced by memristor to
produce tanh activation function and the most efficient circuit
configuration in terms performance metrics is to be determined.
Our design proves that replacing CMOS device by memristor
element improves the circuit performance by reducing the total
power, area of the chip and THD level.
