• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Liu Bicheng, Gu Haifeng, Chen Mingsong, Gu Shouzhen, Chen Wenjie. An Efficient Processing In Memory Framework Based on Skyrmion Material[J]. Journal of Computer Research and Development, 2019, 56(4): 798-809. DOI: 10.7544/issn1000-1239.2019.20180157
Citation: Liu Bicheng, Gu Haifeng, Chen Mingsong, Gu Shouzhen, Chen Wenjie. An Efficient Processing In Memory Framework Based on Skyrmion Material[J]. Journal of Computer Research and Development, 2019, 56(4): 798-809. DOI: 10.7544/issn1000-1239.2019.20180157

An Efficient Processing In Memory Framework Based on Skyrmion Material

More Information
  • Published Date: March 31, 2019
  • As a new computing paradigm, processing in memory (PIM) allows the parallel computation in both processors and memories, which drastically reduce the movements between computation units and storage units. Therefore, PIM can be considered as an efficient technology to somewhat address the shortcomings of the von neumann architecture. Compared with traditional random access memories, racetrack memory has many merits including high density, non-volatility, and low static power. Therefore, it can be used for efficient PIM computing. To address the shortages of domain-wall based PIM, this paper proposes a novel PIM framework based on the Skyrmion material. In this framework, we use Skyrmion-based racetrack memories to construct storage units, and use Skyrmion-based logic gates to compose both adders and multipliers for the computation units. Since our framework does not need CMOS (complementary metal oxide semiconductor) circuits to assist the underlying computation unit construction, the design complexity is significantly reduced. Meanwhile, based on our proposed optimization methods for read and write operations at the circuit layer and address mapping mode of the memory at the system level, the performance of our framework is drastically improved. Experimental results show that compared with domain-wall based PIM framework, our approach can achieve 48.1% time improvement and 42.9% energy savings on average.
  • Related Articles

    [1]Zhang Liping, Liu Lei, Hao Xiaohong, Li Song, Hao Zhongxiao. Voronoi-Based Group Reverse k Nearest Neighbor Query in Obstructed Space[J]. Journal of Computer Research and Development, 2017, 54(4): 861-871. DOI: 10.7544/issn1000-1239.2017.20151111
    [2]Yang Zexue, Hao Zhongxiao. Group Obstacle Nearest Neighbor Query in Spatial Database[J]. Journal of Computer Research and Development, 2013, 50(11): 2455-2462.
    [3]Liu Runtao, Hao Zhongxiao. Fast Algorithm of Nearest Neighbor Query for Line Segments of Spatial Database[J]. Journal of Computer Research and Development, 2011, 48(12): 2379-2384.
    [4]Miao Dongjing, Shi Shengfei, and Li Jianzhong. An Algorithm on Probabilistic Frequent Nearest Neighbor Query over Snapshots of Uncertain Database with Locally Correlation[J]. Journal of Computer Research and Development, 2011, 48(10): 1812-1822.
    [5]Liao Haojun, Han Jizhong, Fang Jinyun. All-Nearest-Neighbor Queries Processing in Spatial Databases[J]. Journal of Computer Research and Development, 2011, 48(1): 86-93.
    [6]Sun Dongpu, Hao Zhongxiao. Group Nearest Neighbor Queries Based on Voronoi Diagrams[J]. Journal of Computer Research and Development, 2010, 47(7): 1244-1251.
    [7]Sun Dongpu, Hao Zhongxiao. Multi-Type Nearest Neighbor Queries with Partial Range Constrained[J]. Journal of Computer Research and Development, 2009, 46(6): 1036-1042.
    [8]Hao Zhongxiao, Wang Yudong, He Yunbin. Line Segment Nearest Neighbor Query of Spatial Database[J]. Journal of Computer Research and Development, 2008, 45(9): 1539-1545.
    [9]Zhang Jing, Lu Hong, and Xue Xiangyang. Efficient Sports Video Retrieval Based on Index Structure[J]. Journal of Computer Research and Development, 2006, 43(11): 1953-1958.
    [10]Dong Daoguo, Liu Zhenzhong, and Xue Xiangyang. VA-Trie: A New and Efficient High Dimensional Index Structure for Approximate k Nearest Neighbor Query[J]. Journal of Computer Research and Development, 2005, 42(12): 2213-2218.
  • Cited by

    Periodical cited type(6)

    1. 徐怡,陶强. 划分序乘积空间约简算法研究. 系统工程理论与实践. 2025(02): 554-570 .
    2. 徐怡,邱紫恒. 基于遗传算法的划分序乘积空间问题求解层选择. 软件学报. 2024(04): 1945-1963 .
    3. 徐怡,张杰. 基于划分序乘积空间的多尺度决策模型. 智能系统学报. 2024(06): 1528-1538 .
    4. 王宝丽,王涛,廉侃超,韩素青. 粒空间中划分知识的正交补研究. 山东大学学报(理学版). 2022(03): 31-40 .
    5. 陈丽芳,代琪,付其峰. 基于粒计算的ELM加权集成算法研究. 华北理工大学学报(自然科学版). 2020(03): 126-132 .
    6. 应申,王子豪,杜志强,丁火平,李翔翔. 数据粒度均衡的二维矢量瓦片构建方法. 地理信息世界. 2020(04): 66-74 .

    Other cited types(12)

Catalog

    Article views (1238) PDF downloads (482) Cited by(18)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return