Implementation of deep neural networks on FPGA-CPU platform using Xilinx SDSOC

Hassan, Rania O.; Mostafa, Hassan

doi:10.1007/s10470-020-01638-5

Cited by 15 publications

(9 citation statements)

References 6 publications

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“…This memory is the main reason for the sluggishness and the weakness in the time complexity of FO-based algorithm. However, the sluggishness and weakness in time complexity would be acceptable for the following reasons: i) The rapid development of implementing a very speed CPUs for data processing could be used in implementing FO-based algorithms for numerous applications [18,21,46]; ii) Many applications have a sample time that is adequate with algorithms depending on FO calculus, for example the power systems [4], the temperature control systems [50,76], and electro-mechanical systems [56]. Moreover, to simplify the computation complexity of the proposed FOPID-FOAC algorithm, the numerical solution of the fractional order differential equation in Eq.…”

Section: Scenario 3: Uncertainty Suppressionmentioning

confidence: 99%

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

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Section: Scenario 3: Uncertainty Suppressionmentioning

confidence: 99%

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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“…Hassan et al [8] presented a HW/SW co-design implementation of AlexNet on an FPGA. They performed the first layer of AlexNet on hardware and achieved 2147483647 clock cycles, which would be approximately 10.7 ms when considering a frequency of 0.2 GHz.…”

Section: Comparisons To Related Workmentioning

confidence: 99%

A Parametrizable High-Level Synthesis Library for Accelerating Neural Networks on FPGAs

Kalms

Rad

Ali

et al. 2021

J Sign Process Syst

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In recent years, Convolutional Neural Network CNN have been incorporated in a large number of applications, including multimedia retrieval and image classification. However, CNN based algorithms are computationally and resource intensive and therefore difficult to be used in embedded systems. FPGA based accelerators are becoming more and more popular in research and industry due to their flexibility and energy efficiency. However, the available resources and the size of the on-chip memory can limit the performance of the FPGA accelerator for CNN. This work proposes an High-Level Synthesis HLS library for CNN algorithms. It contains seven different streaming-capable CNN (plus two conversion) functions for creating large neural networks with deep pipelines. The different functions have many parameter settings (e.g. for resolution, feature maps, data types, kernel size, parallelilization, accuracy, etc.), which also enable compile-time optimizations. Our functions are integrated into the HiFlipVX library, which is an open source HLS FPGA library for image processing and object detection. This offers the possibility to implement different types of computer vision applications with one library. Due to the various configuration and parallelization possibilities of the library functions, it is possible to implement a high-performance, scalable and resource-efficient system, as our evaluation of the MobileNets algorithm shows.

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“…Besides the high-level synthesis (HLS) tool that can compile deep learning C/C++ code for programmable logic in the hardware [251], Xilinx also developed The Deep Learning Processor Unit (DPU) intellectual property (IP) core that can be integrated into the programmable logic of selected Zynq-7000 SoC and Zynq UltraScale+ MPSoC devices with direct connections to the processing system. Specifically, this DPU is a programmable engine dedicated to convolutional neural networks.…”

Section: A Deep Learning Processor Unit (Dpu)mentioning

confidence: 99%

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Zhang¹

2021

Preprint

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This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.

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Implementation of deep neural networks on FPGA-CPU platform using Xilinx SDSOC

Cited by 15 publications

References 6 publications

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

A Parametrizable High-Level Synthesis Library for Accelerating Neural Networks on FPGAs

Artificial Intelligence in Electric Machine Drives: Advances and Trends

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