• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.255-258
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• 2001

This paper is implementation of cellular automata neural network system which is a living creatures' brain using evolving hardware concept. Cellular automata neural network system is based on the development and the evolution, in other words, it is modeled on the ontogeny and phylogeny of natural living things. The proposed system developes each cell's state in neural network by CA. And it regards code of CA rule as individual of genetic algorithm, and evolved by genetic algorithm. In this paper we implement this system using evolving hardware concept Evolving hardware is reconfigurable hardware whose configuration is under the control of an evolutionary algorithm. We design genetic algorithm process for evolutionary algorithm and cells in cellular automata neural network for the construction of reconfigurable system. The effectiveness of the proposed system is verified by applying it to time-series prediction.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.426-432
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• 2001

This paper is implementation of cellular automata neural network system using evolving hardware concept. This system is a living creatures'brain based on artificial life techniques. Cellular automata neural network system is based on the development and the evolution, in other words, it is modeled on the ontogeny and phylogney of natural living things. The phylogenetic mechanism are fundamentally non-deterministic, with the mutation and recombination rate providing a major source of diversity. Ontogeny is deterministic and local physics. Cellular automata is developed from initial cells, and evaluated in given environment. And genetic algorithms take a part in adaptation process. In this paper we implement this system using evolving hardware concept. Evolving hardware is reconfigurable hardware whose configuration si under the control of an evolutionary algorithm. We design genetic algorithm process for evolutionary algorithm and cells in cellular automata neural network for the construction of reconfigurable system. The effectiveness of the proposed system if verified by applying it to Exclusive-OR.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1334-1337
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• 1997

A spacecraft attitude control ground hardware simulator development is discussed in the paper. The simulator is called KT/KARI HILSSAT(Hardware-In-the Loop Simulator Single Axis Testbed), and the main structure consists of a single axis bearing and a satellite main body model on the bearing. The single axis tabel as ans experimental hardware simulator that evaluates performance and applicability of a satellite before evolving and/or confirming a mew or and old control logic used in the KOREASAT is developed. Attitude control of spaceraft by using reaction wheel is performed.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.746-753
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• 2001

In this paper, we review the basic concept of Evolvable Hardware first. And we examine genetic algorithm processor and hardware reconfiguration method and implementation. By considering complexity and performance of hardware at the same time, we design genetic algorithm processor using modularization and parallel processing method. And we design frame that has connection structure and logic block on FPGA, and embody reconfigurable hardware that do so that this frame may be reconstructed by RAM. Also we implemented ECANS that information processing system such as living creatures'brain using this hardware reconfiguration method. And we apply ECANS which is implemented using the concept of Evolvable Hardware to time-series prediction problem in order to verify the effectiveness.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.62-73
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• 2005

Evolvable Hardware is the technique that obtains target function by adapting reconfigurable digital' devices to environment in real time using evolutionary computation. It opens the possibility of automatic design of hardware circuits but still has the limitation to produce complex circuits. In this paper, we have analyzed the fitness landscape of evolvable hardware and proposed a speciation technique of evolving diverse individuals simultaneously, proving the efficiency empirically. Also, we show that useful extra functions can be obtained by analyzing diverse circuits from the speciation technique.

• Interaction and multiscale mechanics
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• v.6 no.2
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• pp.197-210
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• 2013

A fundamental trend of processor architecture evolving towards exaflops is fast increasing floating point performance (so-called "free" flops) accompanied by much slowly increasing memory and network bandwidth. In order to fully enjoy the "free" flops, a numerical algorithm of PDEs should request more flops per byte or increase arithmetic intensity. A meshfree/GFEM approximation can be the class of the algorithm. It is shown in a GFEM without extra dof that the kind of approximation takes advantages of the high performance of manycore GPUs by a high accuracy of approximation; the "expensive" method is found to be reversely hardware-efficient on the emerging architecture of manycore.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.12-22
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• 2023

The development of neural networks is evolving towards the adoption of transformer structures with attention modules. Hence, active research focused on extending the concept of lightweight neural network algorithms and hardware acceleration is being conducted for the transition from conventional convolutional neural networks to transformer-based networks. We present a survey of state-of-the-art research on lightweight neural network algorithms and hardware architectures to reduce memory usage and accelerate both inference and training. To describe the corresponding trends, we review recent studies on token pruning, quantization, and architecture tuning for the vision transformer. In addition, we present a hardware architecture that incorporates lightweight algorithms into artificial intelligence processors to accelerate processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.462-466
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• 2002

Genetic Algorithm (GA) which imitates the process of nature evolution is applied to various fields because it is simple to theory and easy to application. Recently applying GA to hardware, it is to proceed the research of Evolvable Hardware(EHW) developing the structure of hardware and reconstructing it. And it is growing a necessity of GAP that embodies the computation of GA to the hardware. Evolving by GA don't act in the software but in the hardware(GAP) will be necessary for the design of independent EHW. This paper shows the design GAP for fast reconfiguration of EHW.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.25-32
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• 2017

In this paper, we propose the softwareization of broadcasting system. Recently, the topic of industry is the fourth industrial revolution. The fourth industrial revolution is evolving from physical to virtualization. The Industrial Revolution is based on IT technology. Artificial Intelligence (AI), Big Data, and the Internet of Things, which are famous for Alpha Go, are based on software. Among IT, software is the main driver of industrial terrain change. The systemization of software on the basis of cloud environment is proceeding rapidly. System development through softwarization can reduce time to market lead time, hardware cost reduction and manual operation compared to existing hardware system. By developing and implementing broadcasting system such as IPTV based on cloud, lead time for opening service compared to existing hardware system can be shortened by more than 90% and investment cost can be saved by about 40%. In addition, the area of the system can be reduced by 50%. In addition, efficiency can be improved between infrastructures, shortening of trouble handling and ease of maintenance. Finally, we can improve customer experience through rapid service opening.

• ETRI Journal
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• v.42 no.6
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• pp.965-975
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• 2020

This paper proposes a new framework for the development and deployment of honeypots for evolving malware threats. As new technological concepts appear and evolve, attack surfaces are exploited. Internet of things significantly increases the attack surface available to malware developers. Previously independent devices are becoming accessible through new hardware and software attack vectors, and the existing taxonomies governing the development and deployment of honeypots are inadequate for evolving malicious programs and their variants. Malware-propagation and compromise methods are highly automated and repetitious. These automated and repetitive characteristics can be exploited by using embedded reinforcement learning within a honeypot. A honeypot for automated and repetitive malware (HARM) can be adaptive so that the best responses may be learnt during its interaction with attack sequences. HARM deployments can be agile through periodic policy evaluation to optimize redeployment. The necessary enhancements for adaptive, agile honeypots require a new development and deployment framework.