Artificial intelligence has made people’s demands for computer computing efficiency increasingly high. The traditional hardware circuit simulation method for neural morphology computation has problems of unstable performance and excessive power consumption. This research will use non-volatile flash memory cells that are easy to read and write to build a convolutional neural network structure to improve the performance of neural morphological computing. In the experiment, floating-gate transistors were used to simulate neural network synapses to design core cross-array circuits. A voltage subtractor, voltage follower and ReLU activation function are designed based on a differential amplifier. An Iris dataset was introduced in this experiment to conduct simulation experiments on the research circuit. The IMC circuit designed for this experiment has high performance, with an accuracy rate of 96.2% and a recall rate of 60.2%. The overall current power consumption of the hardware circuit is small, and the current power consumption of the subtractor circuit and ReLU circuit does not exceed 100 µA, while the power consumption of the negative feedback circuit is about 440 mA. The accuracy of analog circuits under the IMC architecture is above 93%, the energy consumption is only about 360 nJ, and the recognition rate is about 12 μs. Compared with the classic von Neumann architecture, it reduces the circuit recognition rate and power consumption while meeting accuracy requirements.

More from our Archive

• DOI: 10.3390/en16155682 2023

  Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy

  Xinwei Sun, Yang Zhang, Yongcheng Zhang, Licheng Wang, Kai Wang

• DOI: 10.3390/en16155809 2023

  Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

  Ping Ma, Shuhui Cui, Mingshuai Chen, Shengzhe Zhou, Kai Wang

• DOI: 10.3390/s23156949 2023

  Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers

  Sana Parez, Naqqash Dilshad, Norah Saleh Alghamdi, Turki M. Alanazi, Jong Weon Lee

• DOI: 10.3390/s23156762 2023

  HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications

  Aitizaz Ali, Bander Ali Saleh Al-rimy, Faisal S. Alsubaei, Abdulwahab Ali Almazroi, Abdulaleem Ali Almazroi

• DOI: 10.3390/machines11080774 2023

  Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions

  Vasiliki Balaska, Zoe Adamidou, Zisis Vryzas, Antonios Gasteratos

• DOI: 10.3390/mi14071480 2023

  A Review of the Residual Stress Generation in Metal Additive Manufacturing: Analysis of Cause, Measurement, Effects, and Prevention

  Nabin Bastola, Muhammad P. Jahan, Nithin Rangasamy, Chandra Sekhar Rakurty

• DOI: 10.3390/electronics12143155 2023

  In-Memory Computing Integrated Structure Circuit Based on Nonvolatile Flash Memory Unit

  Peilong Xu, Dan Lan, Fengyun Wang, Incheol Shin

• DOI: 10.3390/s23146507 2023

  CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

  Basim Alabsi, Mohammed Anbar, Shaza Rihan

• DOI: 10.1039/d3mh00882g 2023

  Defect-stabilized and oxygen-coordinated iron single-atom sites facilitate hydrogen peroxide electrosynthesis

  Taotao Gao, Lu Qiu, Minghao Xie, Zhaoyu Jin, Panpan Li, Guihua Yu

• DOI: 10.3390/electronics12153300 2023

  Anomaly Detection in 6G Networks Using Machine Learning Methods

  Mamoon M. Saeed, Rashid A. Saeed, Maha Abdelhaq, Raed Alsaqour, Mohammad Kamrul Hasan, Rania A. Mokhtar