嵌入式异构智能计算系统并行多流水线设计

赵二虎; 吴济文; 肖思莹; 晋振杰; 徐勇军

doi:10.12263/DZXB.20230281

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嵌入式异构智能计算系统并行多流水线设计

学术论文 | 更新时间：2025-12-08

- 嵌入式异构智能计算系统并行多流水线设计
- Parallel Multi Pipeline Design of Embedded Heterogeneous AI Computing Systems
- 电子学报 2023年51卷第11期页码：3354-3364
- 作者机构：
  
  1.中国科学院计算技术研究所专项技术研究中心，北京100190
  2.中国科学院大学，北京100049
- 作者简介：
  
  [ "赵二虎男，1985年生，河北邢台人.现为中国科学院计算技术研究所高级工程师、硕士生导师，中国科学院大学博士研究生.主要研究方向为嵌入式智能计算系统、专用计算机硬件、芯片微体系结构等.E-mail: zhaoerhu@ict.ac.cn" ]
  [ "吴济文男，1987年生，江西上饶人.现为中国科学院计算技术研究所高级工程师.主要研究方向为嵌入式智能计算系统的软硬件协同优化.E-mail: wujiwen@ict.ac.cn" ]
  [ "肖思莹女，1987年生，江西吉安人.现为中国科学院计算技术研究所工程师.主要研究方向为嵌入式智能计算系统软件.E-mail: xiaosiying@ict.ac.cn" ]
  [ "晋振杰男，1991年生，河北邯郸人.现为中国科学院计算技术研究所助理工程师.主要研究方向为嵌入式智能计算系统的AI模型优化加速与部署.E-mail: jinzhenjie@ict.ac.cn" ]
  [ "徐勇军男，1979年生，安徽安庆人.现为中国科学院计算技术研究所正研级高级工程师、博士生导师、专项技术研究中心主任.主要研究方向为数据智能.E-mail: xyj@ict.ac.cn" ]
- 基金信息：
  
  中国科学院技术支撑人才项目;北航杭州创新研究院钱江实验室开放基金(2020-Y8-A-023)
- DOI：10.12263/DZXB.20230281
  中图分类号： TP389.1;
- 收稿：2023-03-30，
  
  修回：2023-10-16，
  
  纸质出版：2023-11-25
- 稿件说明：
移动端阅览
赵二虎,吴济文,肖思莹等.嵌入式异构智能计算系统并行多流水线设计[J].电子学报,2023,51(11):3354-3364.

ZHAO Er-hu,WU Ji-wen,XIAO Si-ying,et al.Parallel Multi Pipeline Design of Embedded Heterogeneous AI Computing Systems[J].ACTA ELECTRONICA SINICA,2023,51(11):3354-3364.
赵二虎,吴济文,肖思莹等.嵌入式异构智能计算系统并行多流水线设计[J].电子学报,2023,51(11):3354-3364. DOI： 10.12263/DZXB.20230281.

ZHAO Er-hu,WU Ji-wen,XIAO Si-ying,et al.Parallel Multi Pipeline Design of Embedded Heterogeneous AI Computing Systems[J].ACTA ELECTRONICA SINICA,2023,51(11):3354-3364. DOI： 10.12263/DZXB.20230281.

摘要

嵌入式智能计算系统因其功耗受限和多传感器实时智能处理需要，对硬件平台的智能算力能效比和智能计算业务并行度提出了严峻挑战.传统嵌入式计算系统常采用的DSP+FPGA数字信号处理架构，无法适用于多个神经网络模型加速场景.本文基于ARM+DLP+SRIO嵌入式异构智能计算架构，利用智能处理器多片多核多内存通道特性，提出了并行多流水线设计方法.该方法充分考虑智能计算业务中数据传输、拷贝、推理、结果反馈等环节时间开销，为不同的神经网络模型合理分配智能算力资源，以达到最大的端到端智能计算业务吞吐率.实验结果表明，采用并行多流水线设计方法的深度学习处理器利用率较单流水线平均提高约25.2%，较无流水线平均提高约30.7%，满足可见光、红外、SAR等多模图像实时智能处理需求，具有实际应用价值.

Abstract

Due to the limited power consumption and the need for real-time intelligent processing of multiple sensors

embedded AI computing systems desire for higher energy efficiency and more parallel intelligent computing services simultaneously.The digital signal processing architecture DSP+FPGA commonly used in traditional embedded computing systems is not suitable for multiple ANN models inference acceleration.Based on embedded heterogeneous intelligent computing architecture ARM+DLP+SRIO

this paper proposes a parallel multi pipeline design method by taking advantage of the characteristics of multi chip

multi-core and multi memory channels of deep learning processors.Considering the time cost of data transmission

copy

reference and feedback

this method allocates intelligent computing resources for different neural network models to achieve the maximum end-to-end throughput.The experimental results show that the utilization of the deep learning processor using the parallel multi pipeline design method is about 25.2% higher than that of a single pipeline

and about 30.7% higher than that without pipeline.It meets the real-time intelligent processing requirements of visible light

infrared and SAR images

and is valuable for practical applications.

关键词

Keywords

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