Hardware Acceleration of Dash Mining Using Dynamic Partial Reconfiguration on the ZYNQ Board

Abdulmonem, Mohamed H.; EssamEddeen, Jihad; Zakhari, Michael H.; Hanafi, Sayed; Mostafa, Hassan

doi:10.1109/icm50269.2020.9331815

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Partial reconfiguration of edge devices is performed to provide metamorphic services. We conducted research on the real-time hardware reconfiguration of edge devices [18,19]. This real-time hardware reconfiguration research showed that the execution flow of an application can be predicted and that the hardware to be executed next to the edge device can be reconfigured in advance [20,21].…”

Section: Related Workmentioning

confidence: 99%

On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices

Lee,

Park

2023

Electronics

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As sensors operating at the edge continue to evolve, the amount of data that edge devices need to process is increasing. Cloud computing methods have been proposed to process complex data on edge devices that are powered by limited resources. However, the existing cloud computing approach, which provides services from servers determined at the compile stage on the edge, is not suitable for the metamorphic edge device proposed in this paper. Therefore, we have realized the operation of metamorphic edge devices by changing the service that accelerates the application in real time according to the surrounding environmental conditions on the edge device. The on-cloud linking approach separates the code for communication from the edge and server into a linkable glue layer. The separated communication code in the linkable glue layer is reconfigured in real time according to the environment of the edge device. To verify the computational acceleration of cloud computing and the real-time service change of the metamorphic edge device, we operated services that perform matrix multiplication operations with one process, two processes, and four processes in parallel on the edge–cloud system based on the on-cloud linking approach. Through the experiments, it was confirmed that the on-cloud linking approach changes the service provided in real time according to changes in external environmental data without changing the code built into the edge. When a square matrix operation with 1000 rows was loaded onto the proposed platform, the size of the code embedded into the edge device decreased by 8.88% and the operation time decreased by 96.7%.

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Section: Related Workmentioning

confidence: 99%

On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices

Lee,

Park

2023

Electronics

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“…The DPR feature in ZYNQ 7020 was utilized to realize different CNNs and SNNs (spiking neural networks) on the same FPGA [29]. In [30], the DPR feature in the ZYNQ 702 board was used to accelerate the cryptocurrency dash mining.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Precision-Scaled CNN Implementation With Dynamic Partial Reconfiguration

et al. 2022

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A convolutional neural network (CNN) classifies images with high accuracy. However, CNN operation requires a large number of computations which consume a significant amount of power when implemented on hardware. Precision scaling has been recently used to reduce the hardware requirements and power consumption. In this paper, we present an energy-efficient precision-scaled CNN (EEPS-CNN) architecture. Furthermore, the Field Programmable Gate Array (FPGA) is reconfigured during run time using Dynamic Partial Reconfiguration (DPR). If the battery level decreases, the EEPS-CNN design with the most appropriate power consumption is configured on the FPGA. DPR enables recognition applications to run at a low power budget while sacrificing minor accuracy instead of termination. The proposed architecture is implemented on Xilinx XC7Z020 FPGA and is evaluated on three datasets: MNIST, F-MNIST, and SVHN datasets. The results show a 2.2X, 2.39X, and 2.38X reduction in the energy consumption, respectively, while using only 7 bits to represent all inputs and network parameters. The accuracy of the proposed EEPS-CNN is only 0.53%, 3.67%, and 0.88% less than 32-bit floating-point architectures for MNIST, F-MNIST, and SVHN, respectively. Moreover, the results show up to 92.91X and 4.84X reductions in the power and energy consumption of the proposed EEPS-CNN compared to related designs developed for the MNIST dataset.

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Hardware-Accelerated Blockchain-Based Authentication for the Internet of Things

Santos

Pascua

Tiglao

2022

Cognitive Radio Oriented Wireless Networks and Wireless Internet

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Hardware Acceleration of Dash Mining Using Dynamic Partial Reconfiguration on the ZYNQ Board

Cited by 3 publications

References 6 publications

On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices

On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices

Energy-Efficient Precision-Scaled CNN Implementation With Dynamic Partial Reconfiguration

Hardware-Accelerated Blockchain-Based Authentication for the Internet of Things

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