An embedded system for handwritten digit recognition

Saldanha, Luca B.; Bobda, Christophe

doi:10.1016/j.sysarc.2015.07.015

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…There must be some sequential execution processes. Therefore, it is necessary to select the most suitable hardware structure to complete the best mapping of the neural network on FPGA [ 29 , 30 ]. As shown in Figure 6 , a combination of parallel and serial is proposed in this design to complete the implementation of BPNN on FPGA.…”

Section: Fpga Design Of Bp Neural Network Pid Algorithmmentioning

confidence: 99%

A Design of FPGA-Based Neural Network PID Controller for Motion Control System

Wang

Jiang

et al. 2022

Sensors

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In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.

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Section: Fpga Design Of Bp Neural Network Pid Algorithmmentioning

confidence: 99%

A Design of FPGA-Based Neural Network PID Controller for Motion Control System

Wang

Jiang

et al. 2022

Sensors

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“…Training and inference of LeNet and ResNet-18 based Handwritten Digits Recognition System (HDRS) is used to resolve the accuracy problem because the variance of handwriting does not cause any problem to human beings. Still, teaching computers to recognize common handwriting is difficult [1] [3].…”

Section: Introductionmentioning

confidence: 99%

A Posit based Handwritten Digits Recognition System

Kumar¹,

Gupta²

2023

IJCDS

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The automated handwritten digit recognition system has numerous applications. It is required to be performed for address interpretation in postal services, bank cheque processing, or digitization of paper documents. But, for computers to recognize the handwritten numeral images is a challenging task. Various techniques have been utilized for this purpose, like convolutional neural networks architectures. This paper presents a novel design of a Posit-based handwritten digits recognition system, one of the convolutional neural network applications. Posit, a universal number system is a substitute of floating point arithmetic format and is hardware friendly. Herein, LeNet and ResNet-18 based HDRS (Handwritten Digits Recognition System) architecture is used for training and inference of model. The parameters obtained after training were converted to (8,0) Posit number system. Training of LeNet and ResNet-18 based HDRS has been done over the MNIST database, an open-source database for handwritten digits recognition. The proposed Posit (8, 0) based HDRS provides comparable accuracy to traditional floating point and fixed point based HDRS.

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“…Improving the accuracy of image classification is one of the most important and interesting problems in machine learning field [1]- [3]. Many researchers carried out various image studies in recent years, such as human face image classification [4]- [6], handwritten digital image recognition [7]- [9], remote sensing image classification [10], [11] and so on. Traditional supervised data classification models almost all based on statistical methodology [12], [13], which first need a user to label numerous samples and then apply these labeled sample points to train the classification model.…”

Section: Introductionmentioning

confidence: 99%

A Novel Active Learning Algorithm for Robust Image Classification

Xiong

Fan

et al. 2020

IEEE Access

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Training samples need to be labeled before being used to train classification model, which usually takes too much labor and material resources. Recently, this problem has attracted widespread attention. In order to reduce the workload of labeling samples, we propose a novel active learning methodology, which uses locally linear reconstruction coefficients to construct semi-supervised data manifold adaptive kernel space. Comparing the new method with other sampling approaches on several real-world image datasets, experimental results indicate that the novel algorithm has preferable classification ability. Especially, it can show higher classification accuracy under the condition that only a few samples are selected to train the classifier model.

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An embedded system for handwritten digit recognition

Cited by 11 publications

References 13 publications

A Design of FPGA-Based Neural Network PID Controller for Motion Control System

A Design of FPGA-Based Neural Network PID Controller for Motion Control System

A Posit based Handwritten Digits Recognition System

A Novel Active Learning Algorithm for Robust Image Classification

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