Design of Artificial Neuron Network with Synapse Utilizing Hybrid CMOS Transistors with Memristor for Low Power Applications
26 Feb 2020-Journal of Circuits, Systems, and Computers (World Scientific Publishing Company)-Vol. 29, Iss: 12, pp 2050187
TL;DR: Neural networks are mimetic with biological neuron which are employed on digital computers and are designed with CMOS technology using 0.45μm in cadence virtuoso.
Abstract: Neural networks are mimetic with biological neuron which are employed on digital computers. These networks are designed with CMOS technology using 0.45μm in cadence virtuoso. The scaling of CMOS li...
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TL;DR: Experiments show that the application of Big Data technology and BP neural network to optimize the piano performance scoring system is effective and can score piano music accurately.
Abstract: At present, there are many chess styles in piano education, but there is a lack of comprehensive, scientific, and guiding teaching mode. It highlights many educational problems and cannot meet the development requirements of piano education at this stage. However, the piano scoring system can partially replace teachers' guidance to piano players. This paper extracts the signal characteristics of playing music, establishes the piano performance scoring model using Big Data and BP neural network technology, and selects famous works to test the effect of the scoring system. The results show that the model can test whether the piano works fairly. It can effectively evaluate the player's performance level and accurately score each piece of music. This not only provides a reference for the player to improve the music level but also provides a new idea for the research results and the application of new technology in music teaching. This paper puts forward reasonable solutions to the problems existing in piano education at the present stage, which is helpful to cultivate high-quality piano talents. Experiments show that the application of Big Data technology and BP neural network to optimize the piano performance scoring system is effective and can score piano music accurately. This paper studies the performance scoring system and gets the model after training, which can replace music teachers and alleviate the shortage of music teachers in the market.
2 citations
TL;DR: The results show that the education integration evaluation model of MC based on AI technology and DL technology can effectively and accurately evaluate the integration effect of MC on the local education system, and then provide reference for local and even national adjustment of education policies.
Abstract: This work intends to solve the problem that the traditional education system cannot reasonably adjust the educational integration of children with the arrival of labor force in a short time, and support the education of migrant children (MC) in the education policy (EP) to integrate them into the local educational environment as soon as possible. Firstly, this work defines the surplus labor force and MC. Secondly, the principles of Artificial Intelligence (AI) and Deep Learning (DL) are introduced. Thirdly, it analyzes the education of MC and relevant policies, and the data of the education effect of MC are collected and the evaluation effect model is built. Finally, the evaluation model of MC’s education effect is applied to test the effect of EP. The results show that using AI technology combined with DL technology to model the education effect of MC can establish an effective and accurate evaluation model of the education effect of MC, effectively evaluate the impact of local education policies on the education of MC, and give an effective effect analysis of relevant education policies in each period. The result of Adaptive Resonance Theory (ART)–Back Propagation algorithm is 65 ∼ 96%, which is much higher than the efficiency of traditional algorithms. This shows that the education integration evaluation model of MC based on AI technology and DL technology can effectively and accurately evaluate the integration effect of MC on the local education system, and then provide reference for local and even national adjustment of education policies. The results provide a new idea for the application of new technology in EP.
2 citations
TL;DR: The research results show that, the agricultural information classification model based on DL and IoT technology can accurately select the required effective information from the network, and the application of IoT technology in agricultural production big data plays an important role in production and economic management.
Abstract: Under the China’s increasing attention to the technological innovation of agricultural production, all kinds of agricultural information have exploded on the internet, and agricultural informatization has developed rapidly. The related information has spread all over the whole network, which makes it gradually difficult to extract useful information from the network. To solve the deficiency of information classification ability of traditional agricultural information collection methods, the classification method of agricultural information is optimized, to realize searching the required agricultural information quickly. At first, the study introduces Deep Learning (DL) technology and the Internet of Things (IoT) and their advantages. Then, based on Bayesian Networks (BN) and Decision Tree (DT) algorithm, the agricultural information classification model is implemented and trained. Using various agricultural economic development theories, analyzation is made on the present situation of domestic agricultural informatization development. Finally, the advantages are put forward of agricultural production informatization development and economic management development based on IoT technology. The research results show that, the agricultural information classification model based on DL and IoT technology can accurately select the required effective information from the network, and the application of IoT technology in agricultural production big data plays an important role in production and economic management. Therefore, the agricultural information classification model based on DL and IoT technology can make an effective and accurate judgment on the classification of agricultural information, and then provide a focus for agricultural production and economic development. A new idea is provided for the application of new technologies in agricultural production and management.
1 citations
01 Mar 2021
TL;DR: In this paper, an electrophysical model and circuit design of an artificial neuron for dopamine-like learning of spiking networks at the hardware level, as well as an analysis of how the parameters of memristive structures influence the features of learning synaptic connections between pairs of such neurons based on them.
Abstract: The development of neuromorphic systems based on spiking neural networks with memristive synaptic weights (nanostructured elements of electrically rewritable nonvolatile memory) is a promising direction in hardware design for solving artificial intelligence problems from the standpoint of significantly reducing energy consumption while increasing the performance of neuromorphic computations. Currently, there is active search for optimal algorithms for such computations and original approaches for their machine learning. One promising option is reinforcement learning using an analog of dopamine modulation observed in the central nervous system of humans and animals. Here we propose an electrophysical model and circuit design of an artificial neuron for dopamine-like learning of spiking networks at the hardware level, as well as an analysis of how the parameters of memristive structures influence the features of learning synaptic connections between pairs of such neurons based on them.
1 citations
Abstract: The memristor-based neural network configuration is a promising approach to realizing artificial neural networks (ANNs) at the hardware level. The memristors can effectively simulate the strength of synaptic connections between neurons in neural networks due to their diverse significant characteristics such as nonvolatility, nanoscale dimensions, and variable conductance. This work presents a new synaptic circuit based on memristors and Complementary Metal Oxide Semiconductor(CMOS), which can realize the adjustment of positive, negative, and zero synaptic weights using only one control signal. The relationship between synaptic weights and the duration of control signals is also explained in detail. Accordingly, Widrow–Hoff algorithm-based memristive neural network (MNN) circuits are proposed to solve the recognition of three types of character pictures. The functionality of the proposed configurations is verified using SPICE simulation.
References
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TL;DR: A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity.
Abstract: A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal−oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.
3,650 citations
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TL;DR: In this article, a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure is presented.
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581 citations