• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1177-1180
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• 2005

This paper present a method of constructing the multimedia processor architecture. The proposed multimedia processor architecture be able to handle each text, sound, and video in one chip. Also it have interactive function that is a characteristics of multimedia. Specially, the proposed multimedia processor be able to addressing nodes in memory map without software, and it is completely reconfigurable depend on data. Also it as able to process time and space common that have synchronous/asynchronous and it is able to protect continuous and dynamic media bus collision, and local and overall common memory structure. The proposed multimedia processor architecture apply to virtual reality and mixed reality.

• 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 the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.61-70
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• 2004

The operation mode of neural network is divided into learning and recognition process. Learning is updating process of weight until neural network archives target result from input pattern. Recognition is arithmetic process of input pattern and weight. Traditional inner product process is focused to improve processing speed and hardware complexity. There is no hardware architecture to distinguish between loaming and recognition mode of neural network. In this paper we propose the new architecture of low power inner product processor for reconfigurable neural network. The proposed architecture is similar with bit-serial inner product processor on learning mode. It have several advantages which are fast processing base on bit-level, suitability of hardware implementation and pipeline architecture to compute data. And proposed architecture minimizes active units and reduces consumption power on recognition mode. Result of simulation shows that active units is depend on bit representation of weight, but we can reduce active units about 50 precent.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.106-111
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• 2011

This paper presents an ASIP (Application-specific Instruction Processor) for motion estimation that employs specific IME instructions and its programmable and reconfigurable hardware architecture for various video codecs, such as H.264/AVC, MPEG4, etc. With the proposed specific instructions and hardware accelerator, it can handle the real-time processing requirement of High Definition (HD) video. With the parallel operations and SAD unit control using pattern information, the proposed IME instruction supports not only full search algorithm but also other fast search algorithms. The hardware size is 77K gates for each Processing Element Group (PEG) which has 256 SAD PEs. The proposed ASIP runs at 160MHz with sixteen PEGs and it can handle 1080p@30 frame in real time.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.3
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• pp.333-340
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• 2017

In this paper, we describe HW/SW co-optimizations for reconfigurable application specific instruction-set processors (ASIPs). Based on our previous very long instruction word (VLIW) ASIP, the proposed framework realizes various forward error-correction (FEC) algorithms for wireless communication systems. In order to enhance the energy efficiency, we newly introduce several design methodologies including high-radix algorithms, task-level out-of-order executions, and intensive resource allocations with loop-level rescheduling. The case study on the radix-4 turbo decoding shows that the proposed techniques improve the energy efficiency by 3.7 times compared to the previous architecture.

• 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.

• The KIPS Transactions:PartA
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• v.12A no.4 s.94
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• pp.281-288
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• 2005

Efficient utilization of processing resources in a large multicomputer system with the possibility of fault occurrence depends on the reliable processor management scheme. This paper presents a dynamic and reliable processor allocation strategy to increase the performance of mesh-connected parallel systems with faulty processors The basic idea is to reconfigure a faulty mesh system into a maximum convex system using the fault-free upper or lower boundary nodes to compensate for the non-boundary faulty nodes. To utilize the non-rectangular shaped system parts, our strategy tries to allocate L-shaped submeshes instead of signaling the allocation failure. Extensive simulations show that the strategy performs more efficiently than other strategies in terms of the job response time md the system utilization.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.324-332
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• 2014

This paper presents a programmable System-On-a-chip for various embedded applications that need Neural Network computations. The system is fully implemented into Field-Programmable Gate Array (FPGA) based prototyping platform. The SoC consists of an embedded processor core and a reconfigurable hardware accelerator for neural computations. The performance of the SoC is evaluated using real image processing applications, such as optical character recognition (OCR) system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2059-2063
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• 2009

This paper presents a programmable System-On-a-chip (SoC) for various embedded smart applications that need Neural Network computations. The system is fully implemented into a prototyping platform based on Field Programmable Gate Array (FPGA). The SoC consists of an embedded processor core and a reconfigurable hardware accelerator for neural computations. The performance of the SoC is evaluated using a real image processing application, an optical character recognition (OCR) system.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.719-722
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• 2005

The synthesis of the 3D images is the most important part of the virtual reality. The photon mapping is the best method for reality in the 3D graphics. This paper presents an architecture for photon mapping applications on SOC devices. The proposed architecture reduces the computation time to photonmap search and radiance estimation. Also this architecture is implemented by a SIMD processor which trades parallelism for frequency of operation.