• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.317-327
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• 2020

R&D(Research and Development) efficiency analysis is a very important issue in academia and industry. Although many studies have been conducted to analyze R&D(Research and Development) efficiency since the past, studies that analyzed R&D(Research and Development) efficiency considering both patentability and patent quality efficiency according to the financial performance of a company do not seem to have been actively conducted. In this study, measuring the patent application and patent quality efficiency according to financial performance, patent quality efficiency according to patent application were applied to corporate groups related to artificial intelligence hardware technology defined as GPU(Graphics Processing Unit), FPGA(Field Programmable Gate Array), ASIC(Application Specific Integrated Circuit) and Neuromorphic. We analyze the efficiency empirically and use Data Envelopment Analysis as a measure of efficiency. This study examines which companies group has high R&D(Research and Development) efficiency about artificial intelligence hardware technology.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• ETRI Journal
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• v.45 no.2
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• pp.254-266
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• 2023

This paper investigates the effect of hardware impairments (HIs) and imperfect channel state information (ICSI) on a SWIPT-assisted adaptive nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) system over independent and nonidentical Rayleigh fading channels. In the NOMA mode, the energy-constrained near users act as a relay to improve the performance for the far users. The OMA transmission mode is adopted to avoid a complete outage when NOMA is infeasible. The best user selection scheme is considered to maximize the energy harvested and avoid error propagation. To characterize the performance of the proposed systems, closed-form and asymptotic expressions of the outage probability for both near and far users are studied. Moreover, exact and approximate expressions of the ergodic rate for near and far users are investigated. Simulation results are provided to verify our theoretical analysis and confirm the superiority of the proposed NOMA/OMA scheme in comparison with the conventional NOMA and OMA protocol with/without HIs and ICSI.

• Electronics and Telecommunications Trends
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• v.33 no.6
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• pp.107-117
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• 2018

In recent years, with the development of autonomous driving technology, high-performance artificial intelligence computing hardware platforms have been developed that can process multi-sensor data, object recognition, and vehicle control for autonomous vehicles. Most of these hardware platforms have been developed overseas, such as NVIDIA's DRIVE PX, Audi's zFAS, Intel GO, Mobile Eye's EyeQ, and BAIDU's Apollo Pilot. In Korea, however, ETRI's artificial intelligence computing platform has been developed. In this paper, we discuss the specifications, structure, performance, and development status centering on hardware platforms that support autonomous driving rather than the overall contents of autonomous driving technology.

• 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).

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.78-81
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• 2019

In this study, we will analyze the design, development and application of these small drones using open source. These drones are used in flight exercises, aerial photography, and coding education. In the era of the fourth industrial revolution, such as the development of sensor technology, expansion of open source sharing, and application of artificial intelligence, Is expected to be able to demonstrate convergence. In this paper, we have studied the design and fabrication of small drones using open source. In the case of drones, various functions and differentiated materials are required depending on the application, and the future development of the unmanned mobile object, namely the drone, in which the creativity and the technology are combined with each other continues to be enhanced by the improvement of autonomy and artificial intelligence. Software-based architecture-based technologies have been developed in collaboration with embedded SWs that combine sensors, motors, and control systems. In hardware, it is customary to use a combination of materials and design to increase the freedom of design. It will be made in a free structure.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.1-11
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• 2023

The emergence of generative hyperscale artificial intelligence (AI) has enabled new services, such as image-generating AI and conversational AI based on large language models. Such services likely lead to the influx of numerous users, who cannot be handled using conventional AI models. Furthermore, the exponential increase in training data, computations, and high user demand of AI models has led to intensive hardware resource consumption, highlighting the need to develop domain-specific semiconductors for hyperscale AI. In this technical report, we describe development trends in technologies for hyperscale AI processors pursued by domestic and foreign semiconductor companies, such as NVIDIA, Graphcore, Tesla, Google, Meta, SAPEON, FuriosaAI, and Rebellions.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1986.04a
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• pp.229-233
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• 1986

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.119-124
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• 2022

Successful underwater localization requires a large-scale, parallel computing environment that can be mounted on various underwater robots. Accordingly, we propose a design method for an artificial intelligence computing platform for underwater localization. The proposed platform consists of a total of four hardware modules. Transponder and hydrophone modules transmit and receive sound waves, and the FPGA module rapidly pre-processes the transmitted and received sound wave signals in parallel. Jetson module processes artificial intelligence based algorithms. We performed a sound wave transmission/reception experiment for underwater localization according to distance in an actual underwater environment. As a result, the designed platform was verified.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.