基於圖靈GPU架構之軟體實體層實作

Implementation of Soft PHY on Turing's GPU

賴永崧(Yung-Sung Lai)

指導教授 ： 許騰尹

國立交通大學/資訊學院/資訊科學與工程研究所/碩士(2020年)

摘要

由於對於網路傳輸效率及品質要求提升，第五代行動通訊網路(5th Generation Mobile Networks)已經成為通訊領域的其中一個鎂光燈，而對於硬體設計，軟體化實體層(soft-PHY)在5G的規格下更能有延展性及擴充性。
然而在Soft-PHY實作的平台下，單純使用CPU及GPU當作系統中的處理器，已經慢慢地無法滿足開發者及使用者的需求，因此為了提高整體GPU平行運算的效率，在開發上除了選擇降低運算的精準度，並且嘗試使用新的GPU圖靈架來取代傳統作法，使得PHY layer能夠有更高的執行效率，因此本篇論文著重於PUSCH在半精度上使用及兩種不同的GPU架構的改良。

關鍵字

長期演進技術 ； 半精度 ； 張量核心

並列摘要

Due to the increase in network transmission efficiency and quality requirements, 5th Generation Mobile Networks has become one of the magnesium lights in the communication field. For hardware design, the soft-PHY layer is used in 5G. Under the specifications can be more malleable and expandable.
However, under the platform implemented by Soft-PHY, simply using the CPU and GPU as the processors in the system has gradually failed to meet the needs of developers and users. Therefore, in order to improve the efficiency of the overall GPU parallel computing, In addition to choosing to reduce the accuracy of the calculation, and trying to use the new GPU Turing frame to replace the traditional method, so that the PHY layer can have higher execution efficiency, this paper focuses on the use of PUSCH in half precision and two different Improved GPU architecture.

並列關鍵字

LTE ； Half-precision Floating-point Format ； Tensor core

參考文獻

[1] 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Multiplexing and channel coding (Release 12)

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