Current Issue : January-March Volume : 2023 Issue Number : 1 Articles : 5 Articles
As CNNs are widely used in fields such as image classification and target detection, the total number of parameters and computation of the models is gradually increasing. In addition, the requirements on hardware resources and power consumption for deploying CNNs are becoming higher and higher, leading to CNN models being restricted to certain specific platforms for miniaturization and practicality. Therefore, this paper proposes a convolutional-neural-network-processor design with an FPGA-based resource-multiplexing architecture, aiming to reduce the consumption of hardware resources and power consumption of CNNs. First, this paper takes a handwritten-digitrecognition CNN as an example of a CNN design based on a resource-multiplexing architecture, and the prediction accuracy of the CNN can reach 97.3 percent by training and testing with Mnist dataset. Then, the CNN is deployed on FPGA using the hardware description language Verilog, and the design is optimized by resource multiplexing and parallel processing. Finally, the total power consumption of the system is 1.03 W and the power consumption of the CNN module is 0.03 W under the premise of guaranteeing the prediction accuracy, and the prediction of a picture is about 68,139 clock cycles, which is 340.7 us under a 200 MHz clock. The experimental results have obvious advantages in terms of resources and power consumption compared with those reported in related articles in recent years, and the design proposed in this paper....
This work proposes a fully parallel hardware architecture of the Naive Bayes classifier to obtain high-speed processing and low energy consumption. The details of the proposed architecture are described throughout this work. Besides, a fixed-point implementation on a Stratix V Field Programmable Gate Array (FPGA) is presented and evaluated regarding the hardware area occupation, processing time (throughput), and dynamic power consumption. In addition, a comparative design analysis was carried out with state-of-the-art works, showing that the proposed implementation achieved a speedup of up to 104× and power savings of up to 107×-times while also reducing the hardware occupancy by up to 102×-times fewer logic cells....
This paper presents an investigation of the transmitting power consumption of a base station (BS) in a simultaneous wireless information and power transfer (SWIPT) system enhanced by a reconfigurable intelligent surface (RIS). The aim is to optimize the total transmitting power consumption when sending information signals and energy from the BS to ground sensors. To this end, the transmitting power consumption of the BS is optimized by satisfying the sensor’s minimum signal-to-interference-plus-noise ratio (SINR), the phase shift constraints of the RIS, and each sensor’s power-splitting (PS) ratio. In order to decouple the optimization variables, we use the technique of block coordinate descent (BCD) to transform the total problem into subproblems. In the second subproblem, the unit modulus constraints are approximated using the successive convex approximation (SCA) method, allowing the optimal solutions to be obtained by solving subproblems in an iterative manner. Our numerical simulation results show that transmitting power consumption can be significantly decreased by adding RIS to an SPWIT system, even in nonlinear harvest models of real application scenarios....
In order to solve the problems of small signal acquisition range and poor acquisition accuracy of the existing multichannel acquisition system, a multisensor energy data fusion transformer acquisition system simulation method based on FPGA is proposed, and key hardware functions are designed and implemented. The system uses FPGA to control the core logic, synchronously collects and controls the energy data of the CCD camera and the laser rangefinder, organizes and uses an external large-capacity SDRAM group for buffering, and uses a dedicated PCI interface chip PLX9656 to achieve high-speed data transmission. Two pieces of sensor energy data and PCI bus energy data are stored in real time using a large-capacity disk array composed of multiple SATA hard disks. The function and performance of the energy data acquisition and storage system were tested. After the actual system test, the experimental results show that the transmission speed of the system through the PCI bus exceeds 200 MB/s, the writing speed of the continuous disk array is 240 MB/s, and the real-time acquisition and recording speed is 100 MB/ss. Conclusion. The system effectively solves the problems of high-speed data acquisition and storage and large capacity data transmission of key sensor nodes....
With the aggravation and evolution of global warming, natural disasters such as hurricanes occur more frequently, posing a great challenge to large-scale power systems. Therefore, the preposition and reconfiguration of the microgrid defense resources by means of Mobile Energy Storage Vehicles (MEVs) and tie lines in damaged scenarios have attracted more and more attention. This paper proposes a novel two-stage optimization model with the consideration of MEVs and tie lines to minimize the shed loads and the outage duration of loads according to their proportional priorities. In the first stage, tie lines addition and MEVs pre-position are decided prior to a natural disaster; in the second stage, the switches of tie lines and original lines are operated and MEVs are allocated from staging locations to allocation nodes according to the specific damaged scenarios after the natural disaster strikes. The proposed load restoration method exploits the benefits of MEVs and ties lines by microgrid formation to pick up more critical loads. The progressive hedging algorithm is employed to solve the proposed scenario-based two-stage stochastic optimization problem. Finally, the effectiveness and superiority of the proposed model and applied algorithm are validated on an IEEE 33-bus test case....
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