• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.1
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• pp.17-24
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• 2013

In this paper, we present a multi-core processor including multimedia specific instructions to process multimedia data efficiently in the mobile environment. Multimedia specific instructions exploit subword level parallelism (SLP), while the multi-core processor exploits data level parallelism (DLP). These combined parallelisms improve the performance of multimedia processing applications. The proposed multi-core processor including multimedia specific instructions is implemented and tested using a Xilinx ISE 10.1 tool and SoCMaster3 testbed system including Vertex 4 FPGA. Experimental results using a fire detection algorithm show that multimedia specific instructions outperform baseline instructions in the same multi-core architecture in terms of performance (1.2x better), energy efficiency (1.37x better), and area efficiency (1.23x better).

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.4
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• pp.231-241
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• 2011

Recently, system virtualization has been applied to consumer electronics (CE) such as smart mobile phones. Although multi-core processors have become a viable solution for complex applications of consumer electronics, the issue of utilizing multi-core resources in the virtualization layer has not been researched sufficiently. In this paper, we present a new hypervisor design and implementation for multi-core CE devices. We concretely describe virtualization methods for a multi-core processor and multi-core-related subsystems. We also analyze bottlenecks of network performance in a virtualization environment that supports multimedia applications and propose an efficient virtual interrupt distributor. Our new multi-core hypervisor improves network performance by 5.5 times as compared to a hypervisor without the virtual interrupt distributor.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.233-238
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• 2016

Recently, the multi-core processor architecture is widely adopted in the embedded processors for enhancing its performance. Multi-core processors are classified either as symmetric or asymmetric. Asymmetric multicore processors are known to score higher performance and more efficient than symmetric multi-core processors. In order to study the performance enhancement of asymmetric multi-core embedded processors over the symmetric ones, the trace-driven simulation has been executed for various asymmetric embedded dual-core, quad-core, octa-core and hexadeca-core processors and compared with the symmetric ones of similar hardware budget using MiBench benchmarks as input.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.163-169
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• 2013

Recently, the importance of embedded system is growing rapidly. In-order to satisfy the real-time constraints of the system, high performance embedded processor is required. Therefore, as in general purpose computer systems, embedded processor should be designed as multicore architecture as well. Using MiBench benchmarks as input, the trace-driven simulation has been performed and analyzed for the 2-core to 16-core embedded processor architectures with different types of cores from simple RISC to in-order and out-of-order superscalar processors, extensively. As a result, the achievable performance is as high as 23 times over the single core embedded RISC processor.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.5
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• pp.539-548
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• 2015

In this paper, shared memory feature was developed in multi-core system with different OS for different processor-specific bus, while conducting an experiment on shared memory feature between the two processors based on embedded Linux system. For the purpose of developing shared memory in dual bus structure, memory controller was used, while managing shared memory segment through list data structure. For AMP multi-core test, Linux OS was installed in 2 processor cores. In addition, it verified the creation and use of shared memory by using kernel module implemented to test shared memory.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.1-10
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• 2017

Recently, with the ever-increasing complexity of industrial robot systems, it has been greatly attention to adopt a multi-core based motion controller with high cost-performance ratio. In this paper, we propose a software architecture that aims to utilize the computing power of multi-core processors. The key concept of our architecture is to use shared memory for the interplay between threads running on separate processor cores. And then, we have integrated our proposed architecture with an industrial standard compliant IDE for automatic code generation of motion runtime. For the performance evaluation, we constructed a test-bed consisting of a motion controller with Preempt-RT Linux based dual-core industrial PC and a 3-axis industrial robot platform. The experimental results show that the actuation time difference between axes is 10 ns in average and bounded up to 689 ns under $1000{\mu}s$ control period, which can come up with real-time performance for industrial robot.

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.68-77
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• 2012

As the system development environment moves from single core to multi core-based platforms, it becomes more important to maintain compatibility between single core-based implementations and multi core-based implementations. Moreover, it is very important to support real-time on multi core platforms especially in cases of embedded software or test equipments which need real-time as well as correctness. Since Windows operating system dopes not support real-time in itself, it has been supporting real-time using expensive third-party solutions such as RTX or INtime. So as to reduce this kind of development expenses, in this paper, we propose RTiK-MP(Real-Time implant Kernel-Multi Processor) which supports real-time on Windows using the Local APIC of x86 architectures, and evaluate the performance of the proposed RTiK-MP after deploying it on portable missile test equipments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.664-668
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• 2010

This paper presents a System-On-a-chip (SoC) for embedded image processing and pattern recognition applications that need Gaussian Pyramid structure. The system is fully implemented into Field-Programmable Gate Array (FPGA) based on the prototyping platform. The SoC consists of embedded processor core and a hardware accelerator for Gaussian Pyramid construction. The performance of the implementation is benchmarked against software implementations on different platforms.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1707-1712
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• 2013

In this paper, we implement a hardware for motor control based on FPGA + embedded processor using Zynq EPP which is All Programmable SoC in order to improve a structural problem of motion control based on such as DSP, MCU and FPGA previously. The implemented motor controller that is fused controller with advantage of FPGA and embedded processor. The signal processing part of high velocity motor control is performed by motor controller based on FPGA. A motion profile and kinematic calculation that are required algorithm process such as operation of a complicate decimal point has processed in an embedded processor based on dual core. As a result of a hardware implementation, it has an advantage that has can be realized an effect of distribution process in one chip. It has also an advantage that is able to organize as a multi-axis motor controller through adding the IP core of motor control implemented on FPGA.

• Journal of Korea Multimedia Society
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• v.19 no.7
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• pp.1154-1158
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• 2016

This paper presents a System-On-a-chip for embedded image processing applications that need Gaussian Pyramid structure. The system is fully implemented into Field-Programmable Gate Array (FPGA) based on the prototyping platform. The SoC consists of embedded processor core and a hardware accelerator for Gaussian Pyramid construction. The performance of the implementation is benchmarked against software implementations on different platforms.