• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.414-420
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• 2005

The ShortStack Micro Server enables any product that contains a microcontroller or microprocessor to quickly and inexpensively become a networked, Internet-accessible device. The ShortStack Micro Server provides a simple way to add LonWorks networking to new or existing smart devices. . It implements the LonTalk protocol and provides the physical interface with the LonWorks communication. The ShortStack host processor can be an 8, 16, or 32-bit microprocessor or microcontrollers. The ShortStack API and driver typically require about 4kbytes of program memory on the host processor and less than 200 bytes of RAM. The interface between host processor and the ShortStack Micro Server may be a Serial Communication Interface (SCI). The LonWorks control module with a high performance is developed, which is composed of the 8 bit PIC Microprocessor for host processor and the smart neuron chip for the ShortStack Micro Server. This intelligent control board is verified as proceeding the various function tests from experimental system with an boost pump and inverter driving systems. It is also confirmed that the developed control module provides stably 0-10VDC linear signal to the input signal of inverter driving system for varying the induction motor speed. Thus, the experimental results show that the fabricating intelligent board carried out very well the various functions in the wide operating ranges of boost pump system. This developed control module expect to apply to industrial fields to require the comparatively exact control and monitoring such as multi-motor driving system with inverter, variable air volume system and the boost pump water supply systems.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.1
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• pp.28-35
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• 2007

In this paper, an ultra low voltage SRAM design method based on dual-boosted cell bias technique is described. For each read/write cycle, the wordline and cell power node of the selected SRAM cells are boosted into two different voltage levels. This enhances SNM(Static Noise Margin) to a sufficient amount without an increase of the cell size, even at sub 1-V supply voltage. It also improves the SRAM circuit speed owing to increase of the cell read-out current. The proposed design technique has been demonstrated through 0.8-V, 32K-byte SRAM macro design in a $0.18-{\mu}m$ CMOS technology. Compared to the conventional cell bias technique, the simulation confirms an 135 % enhancement of the cell SNM and a 31 % faster speed at 0.8-V supply voltage. This prototype chip shows an access time of 23 ns and a power dissipation of $125\;{\mu}W/Hz$.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.10
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• pp.77-83
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• 2004

In low-power VLSI systems, it is effective to suppress leakage current by shutting down megablocks in idle states. Recently, multi-threshold voltage CMOS (MTCMOS) is widely accepted to shutdown power supply. However, it requires short wake-up time as operating frequency increases. This causes large current surge during wake-up process, and it often leads to system malfunction due to severe Power line noise. In this paper, a novel wake-up scheme is proposed to solve this problem. It exploits pipelined wake-up strategy in several stages that reduces maximum current on the power line and its corresponding power line noise. To evaluate its efficiency, the proposed scheme was applied to a multiplier block in the Compact Flash memory controller chip. Power line noise in shutdown and wake-up process was simulated and analyzed. From the simulation results, the proposed scheme was proven to greatly reduce the power line noise compared with conventional schemes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.375-378
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• 2013

A reference voltage generator which is insensitive to PVT (process-voltage-temperature) variation necessary for NVM memory IPs such as EEPROM and MTP memories is designed in this paper. The designed BGR (bandgap reference voltage) circuit based on MagnaChip's $0.18{\mu}m$ EEPROM process uses a low-voltage bandgap reference voltage generator of cascode current-mirror type with a wide swing and shows a reference voltage characteristic insensitive to PVT variation. The minimum operating voltage is 1.43V and the VREF sensitivity against VDD variation is 0.064mV/V. Also, the VREF sensitivity against temperature variation is $20.5ppm/^{\circ}C$. The VREF voltage has a mean of 1.181V and its three sigma ($3{\sigma}$) value is 71.7mV.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.147-156
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• 2023

Electronic voting machines (EVMs) are replacing research ballots due to the errors involved in the manual counting process and the lengthy time required to count the votes. Even though these digital recording electronic systems are advancements, they are vulnerable to tampering and electoral fraud. The suspected vulnerabilities in EVMs are the possibility of tampering with the EVM's memory chip or replacing it with a fake one, their simplicity, which allows them to be tampered with without requiring much skill, and the possibility of double voting. The vote data is shared among all network devices, and peer-to-peer verification is performed to ensure the vote data's authenticity. To successfully tamper with the system, all of the data stored in the nodes must be changed. This improves the proposed system's efficiency and dependability. Elections and voting are fundamental components of a democratic system. Various attempts have been made to make modern elections more flexible by utilizing digital technologies. The fundamental characteristics of free and fair elections are intractability, immutability, transparency, and the privacy of the actors involved. This corresponds to a few of the many characteristics of blockchain-like decentralized ownership, such as chain immutability, anonymity, and distributed ledger. This working research attempts to conduct a comparative analysis of various blockchain technologies in development and propose a 'Blockchain-based Electronic Voting System' solution by weighing these technologies based on the need for the proposed solution. The primary goal of this research is to present a robust blockchain-based election mechanism that is not only reliable but also adaptable to current needs.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.3
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• pp.75-85
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• 2009

This work proposes a 13b 100MS/s 0.13um CMOS ADC for 3G communication systems such as two-carrier W-CDMA applications simultaneously requiring high resolution, low power, and small size at high speed. The proposed ADC employs a four-step pipeline architecture to optimize power consumption and chip area at the target resolution and sampling rate. Area-efficient high-speed high-resolution gate-bootstrapping circuits are implemented at the sampling switches of the input SHA to maintain signal linearity over the Nyquist rate even at a 1.0V supply operation. The cascode compensation technique on a low-impedance path implemented in the two-stage amplifiers of the SHA and MDAC simultaneously achieves the required operation speed and phase margin with more reduced power consumption than the Miller compensation technique. Low-glitch dynamic latches in sub-ranging flash ADCs reduce kickback-noise referred to the differential input stage of the comparator by isolating the input stage from output nodes to improve system accuracy. The proposed low-noise current and voltage references based on triple negative T.C. circuits are employed on chip with optional off-chip reference voltages. The prototype ADC in a 0.13um 1P8M CMOS technology demonstrates the measured DNL and INL within 0.70LSB and 1.79LSB, respectively. The ADC shows a maximum SNDR of 64.5dB and a maximum SFDR of 78.0dB at 100MS/s, respectively. The ABC with an active die area of $1.22mm^2$ consumes 42.0mW at 100MS/s and a 1.2V supply, corresponding to a FOM of 0.31pJ/conv-step.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.96-106
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• 2014

Previous academic research on FPGA tools has relied on simple imaginary models for the targeting architecture. As the first step to overcome such restriction, the issues on analytic placement and legalization which are applied to commercial FPGAs have been brought up, and several techniques to remedy them are presented, and evaluated. First of all, the center of gravity of the placed cells may be far displaced from the center of the chip during analytic placement. A function is proposed to be added to the objective function for minimizing this displacement. And then, the density map is expanded into multiple layers to accurately calculate the density distribution for each of the cell types. Early fixation is also proposed for the memory blocks which can be placed at limited sites in small numbers. Since two flip-flops share control pins in a slice, a compatibility constraint is introduced during legalization. Pre-packing compatible flip-flops is proposed as a proactive step. The proposed techniques are implemented on the K-FPGA fabric evaluation framework in which commercial architectures can be precisely modeled, and modified for enhancement, and validated on twelve industrial strength examples. The placement results show that the proposed techniques have reduced the wire length by 22%, and the slice usage by 5% on average. This research is expected to be a development basis of the optimization CAD tools for new as well as the state-of-the-art FPGA architectures.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.32-40
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• 2016

IEEE 802.15.4 is the one of the protocols for radio communication in a personal area network. Because of low cost and low power communication for IoT communication, it requires the highest optimization level in the implementation. Recently, the studies of location aware algorithm based on IEEE802.15.4 standard has been achieved. Location estimation is performed basically in equal consideration of reference node information and blind node information. However, an error is not calculated in this algorithm despite the fact that the coordinates of the estimated location of the blind node include an error. In this paper, we enhanced a conventual maximum likelihood estimation using weighted coefficient and implement the hardwired location aware engine for small code size and low power consumption. On the field test using test-beds, the suggested hardware based location awareness method results better accuracy by 10 percents and reduces both calculation and memory access by 30 percents, which improves the systems power consumption.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.371-378
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• 2004

This paper presents the implementation and performance of the HVIA-GE card, which is a hardware implementation of the Virtual Interface Architecture (VIA) based on Gigabit Ethernet. The HVIA-GE card is a 32-bit/33MHz PCI adapter containing an FPGA for the VIA protocol engine and a Gigabit Ethernet chip set to construct a high performance physical network. HVIA-GE performs virtual-to-physical address translation, Doorbell, and send/receive completion operations in hardware without kernel intervention. In particular, the Address Translation Table (ATT) is stored on the local memory of the HVIA-GE card, and the VIA protocol engine efficiently controls the address translation process by directly accessing the ATT. As a result, the communication overhead during send/receive transactions is greatly reduced. Our experimental results show the maximum bandwidth of 93.7MB/s and the minimum latency of 11.9${\mu}\textrm{s}$. In terms of minimum latency HVIA-GE performs 4.8 times and 9.9 times faster than M-VIA and TCP/IP, respectively, over Gigabit Ethernet. In addition, the maximum bandwidth of HVIA-GE is 50.4% and 65% higher than M-VIA and TCP/IP respectively.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.200-207
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• 2020

Recently, deep neural networks such as CNN are showing excellent performance in various fields such as image classification, object recognition, visual quality enhancement, etc. However, as the model size and computational complexity of deep learning models for most applications increases, it is hard to apply neural networks to IoT and mobile environments. Therefore, neural network compression algorithms for reducing the model size while keeping the performance have been being studied. In this paper, we apply few compression methods to CNN models and evaluate their performances in the embedded environment. For evaluate the performance, the classification performance and inference time of the original CNN models and the compressed CNN models on the image inputted by the camera are evaluated in the embedded board equipped with QCS605, which is a customized AI chip. In this paper, a few CNN models of MobileNetV2, ResNet50, and VGG-16 are compressed by applying the methods of pruning and matrix decomposition. The experimental results show that the compressed models give not only the model size reduction of 1.3~11.2 times at a classification performance loss of less than 2% compared to the original model, but also the inference time reduction of 1.2~2.21 times, and the memory reduction of 1.2~3.8 times in the embedded board.