• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2633-2636
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• 2003

This study focuses on the new hardware design of fast and low-complexity multiplier over GF(2$\^$m/). The proposed multiplier based on the irreducible all one polynomial (AOP) of degree m, to reduced the system's complexity. It composed of Cyclic Shift, Partial Product, and Modular Summation Blocks. Also it consists of (m＋1)$^2$2-input AND gates and m(m＋1) 2-input XOR gates. Out architecture is very regular, modular and therefore, well-suited for VLSI implementation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.364-370
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• 2011

The Hilbert transform performs a role to transform band pass signals into low pass signals in wireless communication systems. The operation of Hilbert transform is based on a convolution process which is required adding and multiplying calculations. When the Hilbert transform is designed and hardware-implemented at gate level, the adding and multiplying operation requires a high power consumption and a occupation of wide area on a chip. So the results of adding and multiplying operation cause to degrade the performance of implemented system. In this paper, the new Hilbert transformer is proposed, which has a low hardware complexity by application of MAG(Minimum Adder Graph) algorithm. The proposed Hilbert transformer was simulated in ISE environment of Xilinx and showed the reduction of hardware complexity comparing with the number of gate in the conventional Hilbert transformer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.12A
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• pp.1387-1395
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• 2004

Turbo decoder inherently requires large memory and intensive hardware complexity due to iterative decoding, despite of excellent decoding efficiency. To decrease the memory space and reduce hardware complexity, various design issues have to be discussed. In this paper, various design issues on Turbo decoder are investigated and the tradeoffs between the hardware complexity and the performance are analyzed. Through the various simulations on the fixed-length analysis, we decided 5-bits for the received data, 6-bits for a priori information, and 7-bits for the quantization state metric, so the performance gets close to that of infinite precision. The MAX operation which is the main function of Log-MAP decoding algorithm is analyzed and the error correction term for MAX* operation can be efficiently implemented with very small hardware overhead. The size of the sliding window was decided as 32 to reduce the state metric memory space and to achieve an acceptable BER.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.92-100
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• 2009

In this paper, we analyze the hardware architecture of Low Density Parity Check (LDPC) decoder using Log Likelihood Ration-Belief Propagation (LLR-BP) decoding algorithm. Various design issues that affect the decoding performance and the hardware complexity are discussed and the tradeoffs between the hardware complexity and the performance are analyzed. The message data for passing error probability is quantized to 7 bits and among them the fractional part is 4 bits. To maintain the decoding performance, the integer and fractional parts for the intrinsic information is 2 bits and 4 bits respectively. We discuss the alternate implementation of $\Psi$(x) function using piecewise linear approximation. Also, we improve the hardware complexity and the decoding time by applying overlapped scheduling.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1037-1042
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• 2018

The neuromorphic technology has been researched for decades, which learns and processes the information by imitating the human brain. The hardware implementations of neuromorphic systems are configured with highly parallel processing structures and a number of simple computational units. It can achieve high processing speed, low power consumption, and low hardware complexity. Recently, the interests of the neuromorphic technology for low power and small embedded systems have been increasing rapidly. To implement low-complexity hardware, it is necessary to reduce input data dimension without accuracy loss. This paper proposed a low-complexity artificial intelligent engine which consists of parallel neuron engines and a feature extractor. A artificial intelligent engine has a number of neuron engines and its controller to process multimodal sensor data. We verified the performance of the proposed neuron engine including the designed artificial intelligent engines, the feature extractor, and a Micro Controller Unit(MCU).

• ETRI Journal
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• v.27 no.5
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• pp.639-642
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• 2005

This paper proposes two kinds of complexity-reduced algorithms for a low density parity check (LDPC) decoder. First, sequential decoding using a partial group is proposed. It has the same hardware complexity and requires a fewer number of iterations with little performance loss. The amount of performance loss can be determined by the designer, based on a tradeoff with the desired reduction in complexity. Second, an early detection method for reducing the computational complexity is proposed. Using a confidence criterion, some bit nodes and check node edges are detected early on during decoding. Once the edges are detected, no further iteration is required; thus early detection reduces the computational complexity.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.2
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• pp.15-23
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• 2009

This paper presents a novel high-speed, low-complexity flexible 128/64-point $radix-2^4$ FFT/IFFT processor for the applications in high-throughput MIMO-OFDM systems. The high radix multi-path delay feed-back (MDF) FFT architecture provides a higher throughput rate and low hardware complexity by using a four-parallel data-path scheme. The proposed processor not only supports the operation of FFT/IFFT in 128-point and 64-point but can also provide a high data processing rate by using a four-parallel data-path scheme. Furthermore, the proposed design has a less hardware complexity compared with traditional 128/64-point FFT/IFFT processors. Our proposed processor has a high throughput rate of up to 560Msample/s at 140MHz while requiring much smaller hardware expenditure satisfying IEEE 802.11n standard requirements.

• Journal of IKEEE
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• v.18 no.1
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• pp.165-171
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• 2014

This paper presents a low complexity QR decomposition (QRD) architecture for MIMO detector. In the proposed approach, various CORDIC-based QRD algorithms are efficiently combined together to reduce the computational complexity of the QRD hardware. Based on the computational complexity analysis on various QRD algorithms, a low complexity approach is selected at each stage of QRD process. The proposed QRD architecture can be applied to any arbitrary dimension of channel matrix, and the complexity reduction grows with the increasing matrix dimension. Our QR decomposition hardware was implemented using Samsung $0.13{\mu}m$ technology. The numerical results show that the proposed architecture achieves 47% increase in the QAR (QRD Rate/Gate count) with 28.1% power savings over the conventional Householder CORDIC-based architecture for the $4{\times}4$ matrix decomposition.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.249-252
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• 2001

TCM(Trellis Coded Modulation) has soft decision scheme so that BM(Branch Metric) calculates the ED(Euclidean Distance) between the received signal and each code words in signal space. For computing the ED, square and square root computations increase the hardware complexity. Some simplified method is known for convolutional codes with QPSK(Quadrature Phase Shift Keying), PSK(Phase Shift Keying) modulation. But it is not acceptable for QAM (Quadrature Amplitude Modulation)-TCM scheme. In this paper, we suggest that two modified BM computation methods, which is applicable for QAM-TCM. By comparative study, we also assessed two proposed method in the case of hardware complexity and BER (Bit Error Rate) performance.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.1-9
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• 2020

Many cryptographic and error control coding algorithms rely on finite field GF(2m) arithmetic. Hardware implementation of these algorithms needs an efficient realization of finite field arithmetic operations. Finite field multiplication is complicated among the basic operations, and it is employed in field exponentiation and division operations. Various algorithms and architectures are proposed in the literature for hardware implementation of finite field multiplication to achieve a reduction in area and delay. In this paper, a low area and delay efficient semi-systolic multiplier over finite fields GF(2m) using the modified Montgomery modular multiplication (MMM) is presented. The least significant bit (LSB)-first multiplication and two-level parallel computing scheme are considered to improve the cell delay, latency, and area-time (AT) complexity. The proposed method has the features of regularity, modularity, and unidirectional data flow and offers a considerable improvement in AT complexity compared with related multipliers. The proposed multiplier can be used as a kernel circuit for exponentiation/division and multiplication.