Earticle

본 연구에서는 안드로이드 플랫폼을 탑재한 스마트 온도 제어기의 사용자 인터페이스(MMI)시스템에 대한 개 발 방법을 나타낸다. 개발하는 스마트 온도제어기의 MMI 시스템은 온도 그래프 및 데이터를 원거리에서 모바일장 치를 통하여 확인할 수 있도록 무선 WiFI 기능과 연동되는 시스템을 개발한다. 먼저, 스마트 온도제어기의 사용자 인터페이스(MMI)시스템의 하드웨어 인터페이스 회로설계방법을 나타내고, MMI 시스템의 운영 소프트웨어 개발 방 법을 나타낸다. 스마트 온도제어기는 실시간 온도제어기 기능과 MMI 시스템 기능을 수행하는 이중 프로세서를 사용 한다. 이어서 개발된 스마트 온도제어기의 MMI시스템의 실제 작동 시험을 통한 동작 성능을 평가한다. 안드로이드 플랫폼을 탑재함으로서 스마트온도제어기의 개발 기간을 줄일 수 있다.

This paper present developing methods of man-machine interface(MMI) system for smart temperature controller with android platform. This MMI system could communicate with mobile machine. Firstly we present electrical hardware design method of MMI system of smart temperature controller. Smart temperature controller is composed of dual processors. Secondly we develop operating software of MMI system using android development environment. And finally we present verification of MMI systems of smart temperature controller with android platform through field experiment. This MMI system with android platform has rapid development speed due to performance of android platform.

In this paper, a warp scheduler is proposed for the improvement of multi-core stream processor based GP-GPU performance. The proposed warp schedulers are divided into odd and even warps, which are issued separately by applying the dual-warp issue. Furthermore, it can simultaneously process up to four instructions because each warp can issue two instructions through superscalar issue. The superscalar issue has a limitation in that it cannot simultaneously process two instructions having data dependence. To solve this limitation, the warp scheduler determines the instruction issuance by testing the issuing condition of the multi-core processor and the read/write register dependence. For scheduling algorithm, the round-robin algorithm was used. To measure the performance of multi-core stream processors, the Gaussian filter mask processing result of the GP-GPU using the proposed warp scheduler was compared with that of the multi-core CPU on various embedded platforms. The experiment results showed that the processing speed of the GP-GPU using the warp scheduler was 6-7 times faster. The GP-GPU also performed better on an image processing application.

For real-time systems it is important to obtain the accurate worst-case execution time (WCET). Furthermore, how to improve the WCET of applications that run on multicore processors is both significant and challenging as the WCET can be largely affected by the possible inter-core interferences in shared resources such as the shared L2 cache. In order to solve this problem, we propose an innovative approach that adopts a code positioning method to reduce the inter-core L2 cache interferences between the different real-time threads that adaptively run in a multi-core processor by using different strategies. The worst-case-oriented strategy is designed to decrease the worst-case WCET among these threads to as low as possible. The other two strategies aim at reducing the WCET of each thread to almost equal percentage or amount. Our experiments indicate that the proposed multicore-aware code positioning approaches, not only improve the worst-case performance of the real-time threads but also make good tradeoffs between efficiency and fairness for threads that run on multicore platforms.