• Journal of Information Processing Systems
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• v.2 no.3 s.4
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• pp.137-142
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• 2006

A large portion of the embedded system development process involves the integration of hardware and software. Unfortunately, communication across the hardware/software boundary is tedious and error-prone to create. This paper presents an automatic hardware/software interface generation system. As the front-end of hardware/software co-design frameworks, a system designer defines XML specifications for hardware functions. Our system generates hardware/software interfaces including Device Driver, Driver API, and Device Controller from these specifications. Embedded software designers can easily use hardware just like system libraries. Our system reduces the mistakes and errors that can be occurred when a software programmer directly connects software to hardware, and supports balancing labors between hardware developers and software programmers. Moreover, this system can be used as the back-end for a hardware/software co-design framework.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.27-32
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• 2008

This paper presents the implementation of simple genetic algorithm using hardware description language for evolvable hardware embedded system. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. So, it is especially suited to applications where no hardware specifications can be given in advance. Evolvable hardware is based on the idea of combining reconfigurable hardware device with evolutionary computation, such as genetic algorithm. Because of parallel, no function call overhead and pipelining, a hardware genetic algorithm give speedup over a software genetic algorithm. This paper suggests the hardware genetic algorithm for evolvable embedded system chip. That includes simulation results for several fitness functions.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.261-268
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• 1996

Hardware-software codesign becomes improtant to effectively sagisfy perfomrance goals, because designers can trade-off in the way hardware and software components work teogether to exhibit a specified behavior. In this paper, a hardware-software pratitioning algorithm is presetned, in which the system behavioral description containing a mixture of hardware and software components is partitioned into hardware part and software part. The partitioning algorithm tries to minimize the given cost function under constraints on hardware resources or latency. Recursive moving of operations between the hardware and software parts is used to find a near optimum partition and the list scheduling approach is used to estimate the hardware area and latency. Since memory may take substantial protion of the hardware part, memory cost is included in sthe hardware cost. Experimental resutls show that our algorithm is effective.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.20-25
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• 2002

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy for evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.254-254
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• 2000

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy (or evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.7-14
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• 2019

This work presents a virtual prototyping design approach for an area-based image stitching hardware. The virtual hardware obtained from virtual prototyping is equivalent to the conceptual algorithm, yet the conceptual blocks are linked to the actual circuit components including the memory, logic gates, and arithmetic units. Through the proposed method, the overall structure, size, and computation speed of the actual hardware can be estimated in the early design stage. As a result, the optimized virtual hardware facilitates the hardware implementation by eliminating trail design and redundant simulation steps to optimize the hardware performance. In order to verify the feasibility of the proposed method, the virtual hardware of an image stitching platform has been realized, where it required 10,522,368 clock cycles to stitch two $1280{\times}1024$ sized images. Furthermore, with a clock frequency of 250MHz, the estimated computation time of the proposed virtual hardware is 0.877sec, which is 10x faster than the software-based image stitch platform using MATLAB.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.49-56
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• 2011

Estimating optimum hardware capacity of an e-learning system is very important process to grasp reasonable size of designing technique architecture and budget during step of ISP(information strategic planning) and development. It hugely influences cost and quality of the whole project. While investment on information system hardware has been continuously increased, there was no certified hardware capacity estimating method in e-learning system development. A guideline for hardware sizing of information systems was established by Telecommunication Technology Association in 2008. However, the guideline is not appropriate for estimating optimum hardware capacity of an e-learning system because it was designed to provide general standards for estimating hardware capacity of various types of projects. The purpose of this paper is to provide a methodology for estimating optimum hardware capacity in e-learning system development. To develop the methodology, this study, first of all, analyzes two e-learning development projects, in which the guideline was applied to estimate optimum hardware capacity. Then, this study finds out several key factors influencing on hardware capacity. Finally, this study suggests a methodology for estimating optimum hardware capacity of an e-learning system, in which weights for the factors are determined through AHP analysis.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.444-450
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• 1996

A digital hardware architecture for artificial neural network with learning capability is described in this paper. It is a modified hardware architecture known as HANNIBAL(Hardware Architecture for Neural Networks Implementing Back propagation Algorithm Learning). For implementing an efficient neural network hardware, we analyzed various type of multiplier which is major function block of neuro-processor cell. With this result, we design a efficient digital neural network hardware using serial/parallel multiplier, and test the operation. We also analyze the hardware efficiency with logic level simulation. (author). refs., figs., tabs.

• ETRI Journal
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• v.32 no.5
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• pp.657-664
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• 2010

Many embedded multimedia systems employ special hardware blocks to co-process with the main processor. Even though an efficient handling of such hardware blocks is critical on the overall performance of real-time multimedia systems, traditional real-time scheduling techniques cannot afford to guarantee a high quality of multimedia playbacks with neither delay nor jerking. This paper presents a hardware-aware rate monotonic scheduling (HA-RMS) algorithm to manage hardware tasks efficiently and handle special hardware blocks in the embedded multimedia system. The HA-RMS prioritizes the hardware tasks over software tasks not only to increase the hardware utilization of the system but also to reduce the output jitter of multimedia applications, which results in reducing the overall response time.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1187-1188
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• 2008

An area of research called evolvable hardware has recently emerged which combines aspects of evolutionary computation with hardware design and synthesis. Evolvable hardware (EHW) is hardware that can change its own circuit structure by genetic learning to achieve maximum adaptation to the environment. In conventional EHW, the learning is executed by software on a computer. In this paper, we have studied and surveyed a gate-level evolvable hardware chip, by integrating both GA hardware and reconfigurable hardware within a single LSI chip. The chip consists of genetic algorithm(GA) hardware, reconfigurable hardware logic, and the control logic. In this paper, we describe the architecture, functions of the chip.