• Journal of Information Processing Systems
• /
• v.8 no.1
• /
• pp.133-144
• /
• 2012

In order to make cognitive radio systems a practical technology to be deployed in real-world scenarios, the core Software Defined Radio (SDR) systems must meet the stringent requirements of the target application, especially in terms of performance and energy consumption for mobile platforms. In this paper we present a feasibility study of hardware acceleration as an energy-efficient implementation for SDR. We identified the amplifier function from the Software Communication Architecture (SCA) for hardware acceleration since it is one of the functions called for most frequently and it requires intensive floating-point computation. Then, we used the Virtex5 Field-Programmable Gate Array (FPGA) to perform a comparison between compiler floating-point support and the on-chip floating-point support. By enabling the on-chip floating-point unit (FPU), we obtained as high as a 2X speedup and 50% of the overall energy reduction. We achieved this with an increase of the power consumption by no more than 0.68%. This demonstrates the feasibility of the proposed approach.

• ETRI Journal
• /
• v.28 no.3
• /
• pp.320-328
• /
• 2006

Using biometrics to verify a person's identity has several advantages over the present practice of personal identification numbers (PINs) and passwords. To gain maximum security in a verification system using biometrics, the computation of the verification as well as the storing of the biometric pattern has to take place in a smart card. However, there is an open issue of integrating biometrics into a smart card because of its limited resources (processing power and memory space). In this paper, we propose a speaker verification algorithm using a support vector machine (SVM) with a very few features, and implemented it on a 32-bit smart card. The proposed algorithm can reduce the required memory space by a factor of more than 100 and can be executed in real-time. Also, we propose a hardware design for the algorithm on a field-programmable gate array (FPGA)-based platform. Based on the experimental results, our SVM solution can provide superior performance over typical speaker verification solutions. Furthermore, our FPGA-based solution can achieve a speed-up of 50 times over a software-based solution.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.414-426
• /
• 2022

Elliptic Curve Cryptography (ECC) is one of the finest cryptographic technique of recent time due to its lower key length and satisfactory performance with different hardware structures. In this paper, a High Throughput Multiplier architecture is introduced for Elliptic Cryptographic applications based on concurrent computations. With the aid of the concurrent computing approach, the High Throughput Concurrent Computation (HTCC) technology that was just presented improves the processing speed as well as the overall efficiency of the point-multiplier architecture. Here, first and second distinct group operation of point multiplier are combined together and synthesised concurrently. The synthesis of proposed HTCC technique is performed in Xilinx Virtex - 5 and Xilinx Virtex - 7 of Field-programmable gate array (FPGA) family. In terms of slices, flip flops, time delay, maximum frequency, and efficiency, the advantages of the proposed HTCC point multiplier architecture are outlined, and a comparison of these advantages with those of existing state-of-the-art point multiplier approaches is provided over GF(2¹⁶³), GF(2²³³) and GF(2²⁸³). The efficiency using proposed HTCC technique is enhanced by 30.22% and 75.31% for Xilinx Virtex-5 and by 25.13% and 47.75% for Xilinx Virtex-7 in comparison according to the LC design as well as the LL design, in their respective fashions. The experimental results for Virtex - 5 and Virtex - 7 over GF(2²³³) and GF(2²⁸³)are also very satisfactory.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1527-1539
• /
• 2015

In this paper the hardware implementation of the direct torque control based on the fuzzy logic technique of induction motor on the Field-Programmable Gate Array (FPGA) is presented. Due to its complexity, the fuzzy logic technique implemented on a digital system like the DSP (Digital Signal Processor) and microcontroller is characterized by a calculating delay. This delay is due to the processing speed which depends on the system complexity. The limitation of these solutions is inevitable. To solve this problem, an alternative digital solution is used, based on the FPGA, which is characterized by a fast processing speed, to take the advantage of the performances of the fuzzy logic technique in spite of its complex computation. The Conventional Direct Torque Control (CDTC) of the induction machine faces problems, like the high stator flux, electromagnetic torque ripples, and stator current distortions. To overcome the CDTC problems many methods are used such as the space vector modulation which is sensitive to the parameters variations of the machine, the increase in the switches inverter number which increases the cost of the inverter, and the artificial intelligence. In this paper an intelligent technique based on the fuzzy logic is used because it is allows controlling the systems without knowing the mathematical model. Also, we use a new method based on the Xilinx system generator for the hardware implementation of Direct Torque Fuzzy Control (DTFC) on the FPGA. The simulation results of the DTFC are compared to those of the CDTC. The comparison results illustrate the reduction in the torque and stator flux ripples of the DTFC and show the Xilinx Virtex V FPGA performances in terms of execution time.

• Journal of Computing Science and Engineering
• /
• v.12 no.1
• /
• pp.24-35
• /
• 2018

Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.

• Nuclear Engineering and Technology
• /
• v.53 no.10
• /
• pp.3327-3334
• /
• 2021

Present electric grids are advanced to integrate smart grids, distributed resources, high-speed sensing and control, and other advanced metering technologies. Cybersecurity is one of the challenges of the smart grid and nuclear plant digital system. It affects the advanced metering infrastructure (AMI), for grid data communication and controls the information in real-time. The research article is emphasized solving the nuclear and smart grid hardware security issues with the integration of field programmable gate array (FPGA), and implementing the latest Time Authenticated Cryptographic Identity Transmission (TACIT) cryptographic algorithm in the chip. The cryptographic-based encryption and decryption approach can be used for a smart grid distribution system embedding with FPGA hardware. The chip design is carried in Xilinx ISE 14.7 and synthesized on Virtex-5 FPGA hardware. The state of the art of work is that the algorithm is implemented on FPGA hardware that provides the scalable design with different key sizes, and its integration enhances the grid hardware security and switching. It has been reported by similar state-of-the-art approaches, that the algorithm was limited in software, not implemented in a hardware chip. The main finding of the research work is that the design predicts the utilization of hardware parameters such as slices, LUTs, flip-flops, memory, input/output blocks, and timing information for Virtex-5 FPGA synthesis before the chip fabrication. The information is extracted for 8-bit to 128-bit key and grid data with initial parameters. TACIT security chip supports 400 MHz frequency for 128-bit key. The research work is an effort to provide the solution for the industries working towards embedded hardware security for the smart grid, power plants, and nuclear applications.

• Journal of the Institute of Convergence Signal Processing
• /
• v.13 no.4
• /
• pp.211-219
• /
• 2012

In this paper, we described the software development for verifying the integrity of output data of TCP/IP for VLBI Correlation Subsystem (VCS) correlation block and proposed the performance improvement method in order to prevent the data loss of correlation output. The VCS correlation results are saved at the Data Archive system through TCP/IP packet transmission. In this paper, the integrity verification software is developed so as to confirm the integrity of correlation result saved at the data archive system using TCP/IP packet information of VCS. The 3-step integrity verification process is proposed by using the developed software, its effectiveness was confirmed in consequence of correlation experiments. In addition, TCP/IP packet transmission must be completed within minimum integration period. However, there is not only TCP/IP packet loss occurred but also the problem of correlation result integrity affected in account of a large quantity of packets and data during short integration time. In this paper, the reason of TCP/IP packet loss is analyzed and the modified methods for FPGA(Field Programmable Gate Array) of VCS are proposed, the integrity problem of correlation results will be solved.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.6
• /
• pp.49-55
• /
• 2021

Recently, a small tracking radar requires the development of a small millimeter wave tracking radar having a high range resolution that can acquire and track a target in various environments and disable the target system with a single blow. Small millimeter wave tracking radar with high range resolution needs to implement a signal processor that can process wide bandwidth signals in real time and meet the requirements of small tracking radar. In this paper, we designed a signal processor that can perform the role and function of a signal processor for a small millimeter wave tracking radar. The signal processor for the small millimeter wave tracking radar requires the real-time processing of input signal of OOOMHz center frequency and OOOMHz bandwidth from 8 channels. In order to satisfy the requirements of the signal processor, the signal processor was designed by applying the high-performance FPGA (Field Programmable Gate Array) and ADC (Analog-to-digital converter) for pre-processing operations, such as DDC (Digital Down Converter) and FFT (Fast Fourier Transform). Finally, the signal processor of the small millimeter wave tracking radar was verified via performance test.

• Journal of Advanced Navigation Technology
• /
• v.26 no.4
• /
• pp.211-218
• /
• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.17 no.1
• /
• pp.110-119
• /
• 2017

This paper describes a CMOS image sensor (CIS) with dual correlated double sampling (CDS) and column-parallel analog-to-digital converter (ADC) and its measurement method using a field-programmable gate array (FPGA) integrated module. The CIS is composed of a $320{\times}240$ pixel array with $3.2{\mu}m{\times}3.2{\mu}m$ pixels and column-parallel 10-bit single-slope ADCs. It is fabricated in a $0.11-{\mu}m$ CIS process, and consumes 49.2 mW from 1.5 V and 3.3 V power supplies while operating at 6.25 MHz. The measured dynamic range is 53.72 dB, and the total and column fixed pattern noise in a dark condition are 0.10% and 0.029%. The maximum integral nonlinearity and the differential nonlinearity of the ADC are +1.15 / -1.74 LSB and +0.63 / -0.56 LSB, respectively.