High-Precision Precharge Control Circuit for SRAM in Convolutional Neural Network Processor

Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading to reduced positioning accuracy of magnetic-assisted navigation systems. Therefore, there is an urgent need for methods which screen out undisturbed geomagnetic field data for realizing the high accuracy pedestrian inertial navigation indoors. In this paper, we propose an algorithm based on a one-dimensional convolutional neural network (1D CNN) to screen magnetic field data. By encoding the magnetic data within a certain time window to a time series, a 1D CNN with two convolutional layers is designed to extract data features. In order to avoid errors arising from artificial labels, the feature vectors will be clustered in the feature space to classify the magnetic data using unsupervised methods. Our experimental results show that this method can distinguish the geomagnetic field data from indoors disturbed magnetic data well and further significantly improve the calculation accuracy of the heading angle. Our work provides a possible technical path for the realization of high-precision indoor pedestrian navigation systems.

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Short Term Electric Load Forecasting Using High Precision Convolutional Neural Network

2020 International Conference on Computer, Electrical & Communication Engineering (ICCECE) ◽

10.1109/iccece48148.2020.9223102 ◽

2020 ◽

Author(s):

Shafiul Hasan Rafi ◽

Nahid-Al-Masood

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Precision ◽

Load Forecasting ◽

Electric Load ◽

Short Term ◽

Electric Load Forecasting

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A high-precision shallow Convolutional Neural Network based strategy for the detection of Genomic Deletions

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2016.7822793 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jing Wang ◽

Cheng Ling ◽

Jingyang Gao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Precision ◽

Genomic Deletions

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14.5 Envision: A 0.26-to-10TOPS/W subword-parallel dynamic-voltage-accuracy-frequency-scalable Convolutional Neural Network processor in 28nm FDSOI

2017 IEEE International Solid-State Circuits Conference (ISSCC) ◽

10.1109/isscc.2017.7870353 ◽

2017 ◽

Cited By ~ 100

Author(s):

Bert Moons ◽

Roel Uytterhoeven ◽

Wim Dehaene ◽

Marian Verhelst

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Processor ◽

Dynamic Voltage

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Memristor Based Binary Convolutional Neural Network Architecture With Configurable Neurons

Frontiers in Neuroscience ◽

10.3389/fnins.2021.639526 ◽

2021 ◽

Vol 15 ◽

Author(s):

Lixing Huang ◽

Jietao Diao ◽

Hongshan Nie ◽

Wei Wang ◽

Zhiwei Li ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Precision ◽

Network Architecture ◽

Large Scale ◽

Network Performance ◽

Recognition Accuracy ◽

Activation Function ◽

Data Set ◽

Neuron Activation

The memristor-based convolutional neural network (CNN) gives full play to the advantages of memristive devices, such as low power consumption, high integration density, and strong network recognition capability. Consequently, it is very suitable for building a wearable embedded application system and has broad application prospects in image classification, speech recognition, and other fields. However, limited by the manufacturing process of memristive devices, high-precision weight devices are currently difficult to be applied in large-scale. In the same time, high-precision neuron activation function also further increases the complexity of network hardware implementation. In response to this, this paper proposes a configurable full-binary convolutional neural network (CFB-CNN) architecture, whose inputs, weights, and neurons are all binary values. The neurons are proportionally configured to two modes for different non-ideal situations. The architecture performance is verified based on the MNIST data set, and the influence of device yield and resistance fluctuations under different neuron configurations on network performance is also analyzed. The results show that the recognition accuracy of the 2-layer network is about 98.2%. When the yield rate is about 64% and the hidden neuron mode is configured as −1 and +1, namely ±1 MD, the CFB-CNN architecture achieves about 91.28% recognition accuracy. Whereas the resistance variation is about 26% and the hidden neuron mode configuration is 0 and 1, namely 01 MD, the CFB-CNN architecture gains about 93.43% recognition accuracy. Furthermore, memristors have been demonstrated as one of the most promising devices in neuromorphic computing for its synaptic plasticity. Therefore, the CFB-CNN architecture based on memristor is SNN-compatible, which is verified using the number of pulses to encode pixel values in this paper.

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Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting

Advances in Civil Engineering ◽

10.1155/2020/9564287 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Qiushuang Lin ◽

Chunxiang Li ◽

Chao Wu

Keyword(s):

Neural Network ◽

Structural Health Monitoring ◽

Convolutional Neural Network ◽

Monitoring System ◽

High Precision ◽

Health Monitoring ◽

Wind Engineering ◽

Stage Model ◽

Health Monitoring System ◽

Non Gaussian

Wind signal forecasting has become more and more crucial in the structural health monitoring system and wind engineering recently. It is a challenging subject owing to the complicated volatility of wind signals. The robustness and generalization of a predictor are significant as well as of high precision. In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity. Afterwards, reinforced forecasting is adopted to enhance the robustness of the preliminary forecasting. The preliminary forecast results by adaptive residual CNN are integrated with historical observed signals as the new input to reconstruct a new forecasting mapping. Meanwhile, simplified-boost strategy is applied for more generalized results. The results of multistep forecasting for five kinds of nonstationary non-Gaussian wind signals prove the more excellent adaptivity and robustness of the developed two-stage model compared with single models.

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