Build-in compact and efficient temperature sensor array on field programmable gate array

Wang, Sheng; Feng, Shiwei; Xiao, Yu‐Xuan; Hu, Chaoxu; Pan, Shijie

doi:10.1016/j.mejo.2021.105018

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…The basic structure of FPGA includes programmable input and output unit, configurable logic block, digital clock management module, embedded block RAM, wiring resources, embedded special hardcore, and underlying embedded functional unit. FPGA has been expanded in digital circuit design because of its rich wiring resources, high repeatability, high integration, and low investment [18]. The system structure is designed based on the actual situation after the performance of the main control chip FPGA, image sensor, DDR SDRAM (Double Data Rate Synchronous Dynamic Random Access Memory) are understood.…”

Section: Overall Acquisition and Detection Systemmentioning

confidence: 99%

Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

Hu¹,

Si²

2021

Preprint

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The aim is to further improve the efficiency of iris detection and ensure real-time iris data acquisition. Here, the light field refocusing algorithm can collect the data in real-time based on the existing iris data acquisition and detection system, and the DL (Deep Learning) CNN (Convolutional Neural Network) is introduced. Consequently, an iris image acquisition and real-time detection system based on CNN is proposed, and the system for image acquisition, processing, and displaying is constructed based on FPGA (Field Programmable Gate Array). The spatial filtering algorithm can compare the performance of the proposed bilateral filters with common filters. The results indicate that the proposed bilateral filters can pick out qualified iris images in real-time, greatly improving the accuracy of the iris image recognition system. The average time for real-time quality assessment of each frame image is less than 0.05 seconds. The classification accuracy of the iris image quality assessment algorithm based on DL is 96.38%, higher than the other two algorithms, and the average classification error rate is 3.69%, lower than the average error rate of other algorithms. The results can provide a reference for real-time iris image detection and data acquisition.

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Section: Overall Acquisition and Detection Systemmentioning

confidence: 99%

Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

Hu¹,

Si²

2021

Preprint

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“…A fuzzy clustering map was presented in [30] to measure the on-chip temperature by using 6 × 8 thermal array sensors. Many other articles deal with the problem of allocation of thermal sensors on FPGAs, which we quote [21,[31][32][33]. However, the approaches proposed in the articles required prior knowledge, which makes them time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Accurate On-Chip Thermal Peak Detection Based on Heuristic Algorithms and Embedded Temperature Sensors

et al. 2023

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The reliability and lifetime of systems-on-chip (SoCs) are being seriously threatened by thermal issues. In modern SoCs, dynamic thermal management (DTM) uses the thermal data captured by thermal sensors to constantly track the hot spots and thermal peak locations in real time. Estimating peak temperatures and the location of these peaks can play a crucial role for DTM systems, as temperature underestimation can cause SoCs to fail and have shortened lifetime. In this paper, a novel sensor allocation algorithm (called thermal gradient tracker, TGT), based on the recursive elimination of regions that likely do not contain any thermal peaks, is proposed for determining regions that potentially contain thermal peaks. Then, based on an empirical source temperature detection technique called GDS (gradient direction sensor), a hybrid algorithm for detecting the position and temperature of thermal peaks is also proposed to increase the accuracy of temperature sensing while trying to keep the number of thermal sensors to a minimum. The essential parameters, H and R, of the GDS technique are determined using an automated search algorithm based on simulated annealing. The proposed algorithm has been applied in a system-on-chip (SoC) in which four heat sources are present, and for temperatures ranging between 45 °C and 115 °C, in a chip area equal to 25 mm2. The simulation results show that our proposed sensor allocation scheme can detect on-chip peaks with a maximum error of 1.48 °C and an average maximum error of 0.49 °C by using 15 thermal sensors.

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The analysis of Iris image acquisition and real-time detection system using convolutional neural network

Liu,

Xu,

Yee

2023

J Supercomput

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Build-in compact and efficient temperature sensor array on field programmable gate array

Cited by 7 publications

References 7 publications

Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

Accurate On-Chip Thermal Peak Detection Based on Heuristic Algorithms and Embedded Temperature Sensors

The analysis of Iris image acquisition and real-time detection system using convolutional neural network

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