PENG Yuanxi, ZHOU Feng, HAI Yue, WANG Yaohua. A Multi-instruction Streams Extension Mechanism for SIMD Processor[J]. Chinese Journal of Electronics, 2017, 26(6): 1154-1160. doi: 10.1049/cje.2017.09.013
Citation: PENG Yuanxi, ZHOU Feng, HAI Yue, WANG Yaohua. A Multi-instruction Streams Extension Mechanism for SIMD Processor[J]. Chinese Journal of Electronics, 2017, 26(6): 1154-1160. doi: 10.1049/cje.2017.09.013

A Multi-instruction Streams Extension Mechanism for SIMD Processor

doi: 10.1049/cje.2017.09.013
Funds:  This work is supported by the National Natural Science Foundation of China (No.61402493), and the Research Project of National University of Defense Technology (No.GC-14-06-02).
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  • Corresponding author: WANG Yaohua (corresponding author) was born in 1985. He received the Ph.D. degree in electronic engineering from National University of Defense Technology. He is a research Associate in college of computer science, National University of Defense Technology.(Email:nudtyh@gmail.com)
  • Received Date: 2015-08-24
  • Rev Recd Date: 2016-06-23
  • Publish Date: 2017-11-10
  • Multi-media applications contain multibranches loop, it is of low efficiency to map them into traditional Single instruction multiple data (SIMD) structures. Considering the above matter, we proposed a multiinstruction streams extension method for traditional SIMD structures. The main idea is to simultaneously dispatch multiple instruction streams to multiple lanes. Compared with traditional SIMD whose lanes receive the unified single instruction stream but execute conditionally through a lane mask vector, Multi-instruction streams extension grants each of its lanes the ability to receive and execute the instructions of one particular branch path. Thus, it is of high efficiency to map multi-branches loop in applications. The design is finally implemented through Verilog language, and then integrated into the FT-Matrix vector-SIMD chip. Application profiling results shows that the proposed method consumes mere 2.61% area overhead while obtains about 1.8x to 2.4x performance gain.
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