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A group-remapping encoding method for low-power GPU data transmission.

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Title: A group-remapping encoding method for low-power GPU data transmission.
Authors: ZHANG, Tiefei1, XING, Jianguo1
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. May2026, Vol. 48 Issue 5, p803-809. 7p.
Subjects: Encoding, Data transmission systems, Energy consumption, Digital communications
Abstract: The high-performance computing capabilities of modern graphics processing unit (GPU) rely on high-bandwidth graphics double data rate (GDDR) interfaces. The high data transfer rates result in significant energy consumption, particularly due to the asymmetric power consumption associated with transmitting logic 1 values in GDDR's pseudo open drain (POD) I/O interfaces. By reducing the number of logic 1 values with high energy consumption during data transfer, the issue of high energy consumption during data transfer can be alleviated. This paper proposes a group-remapping encoding method based on the quantity of logic 1 values. Initially, the data to be transmitted is divided into basic units of 4 bits each, which are then grouped according to the number of logic 1 values they contain. Subsequently, groups with a higher quantity of logic 1 values are mapped and encoded into groups with a lower quantity of logic 1 values, aiming to minimize the global count of logic 1 values. When evaluated on modern GPU architectures, the results demonstrate that the group-remapping encoding strategy effectively reduces the number of logic 1 values during data transmission for various applications, achieving an average reduction rate of 26%, thereby proving the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: The high-performance computing capabilities of modern graphics processing unit (GPU) rely on high-bandwidth graphics double data rate (GDDR) interfaces. The high data transfer rates result in significant energy consumption, particularly due to the asymmetric power consumption associated with transmitting logic 1 values in GDDR's pseudo open drain (POD) I/O interfaces. By reducing the number of logic 1 values with high energy consumption during data transfer, the issue of high energy consumption during data transfer can be alleviated. This paper proposes a group-remapping encoding method based on the quantity of logic 1 values. Initially, the data to be transmitted is divided into basic units of 4 bits each, which are then grouped according to the number of logic 1 values they contain. Subsequently, groups with a higher quantity of logic 1 values are mapped and encoded into groups with a lower quantity of logic 1 values, aiming to minimize the global count of logic 1 values. When evaluated on modern GPU architectures, the results demonstrate that the group-remapping encoding strategy effectively reduces the number of logic 1 values during data transmission for various applications, achieving an average reduction rate of 26%, thereby proving the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3969/j.issn.1007-130X.2026.05.005
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      – Code: chi
        Text: Chinese
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        PageCount: 7
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      – SubjectFull: Encoding
        Type: general
      – SubjectFull: Data transmission systems
        Type: general
      – SubjectFull: Energy consumption
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      – SubjectFull: Digital communications
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              Text: May2026
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              Y: 2026
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