• Title/Summary/Keyword: L1 캐시 메모리

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Design of Cache Memory System for Next Generation CPU (차세대 CPU를 위한 캐시 메모리 시스템 설계)

  • Jo, Ok-Rae;Lee, Jung-Hoon
    • IEMEK Journal of Embedded Systems and Applications
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    • v.11 no.6
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    • pp.353-359
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    • 2016
  • In this paper, we propose a high performance L1 cache structure for the high clock CPU. The proposed cache memory consists of three parts, i.e., a direct-mapped cache to support fast access time, a two-way set associative buffer to reduce miss ratio, and a way-select table. The most recently accessed data is stored in the direct-mapped cache. If a data has a high probability of a repeated reference, when the data is replaced from the direct-mapped cache, the data is stored into the two-way set associative buffer. For the high performance and fast access time, we propose an one way among two ways set associative buffer is selectively accessed based on the way-select table (WST). According to simulation results, access time can be reduced by about 7% and 40% comparing with a direct cache and Intel i7-6700 with two times more space respectively.

Accelerating Medical Image Processing on Integrated GPU Using OpenCL (OpenCL을 이용한 내장형 GPU에서의 의학영상처리 가속화)

  • Kim, Beom-Jun;Shin, Byeong-seok
    • Journal of the Korea Computer Graphics Society
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    • v.23 no.2
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    • pp.1-10
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    • 2017
  • A variety of filters are applied to improve the quality of noise and low resolution medical images. This is necessary to reduce the radiation dose of the patient and to improve the utilization of the conventional spherical imaging equipment. In the conventional method, it is common to perform filtering using the CPU of the PC. However, it is difficult to produce results in real time by applying various calculations and filters to high-resolution human images using only the CPU performance of a PC used in a hospital. In this paper, we analyze the structure and performance of Intel integrated GPU in CPU and propose a method to perform image filtering using OpenCL parallel processing function. By applying complex filters with high computational complexity to medical images, high quality images can be generated in real time.