Memory streaming acceleration for embedded systems with CPU-accelerator cooperative data processing

Kwangho Lee, Joonho Kong, Young Geun Kim, Sung Woo Chung

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Memory streaming operations (i.e., memory-to-memory data transfer with or without simple arithmetic/logical operations) are one of the most important tasks in general embedded/mobile computer systems. In this paper, we propose a technique to accelerate memory streaming operations. The conventional way to accelerate memory streaming operations is employing direct memory access (DMA) with dedicated hardware accelerators for simple arithmetic/logical operations. In our technique, we utilize not only a hardware accelerator with DMA but also a central processing unit (CPU) to perform memory streaming operations, which improves the performance and energy efficiency of the system. We also implemented our prototype in a field-programmable gate array system-on-chip (FPGA-SoC) platform and evaluated our technique in real measurement from our prototype. From our experimental results, our technique improves memory streaming performance by 34.1–73.1% while reducing energy consumption by 29.0–45.5%. When we apply our technique to various real-world applications such as image processing, 1 × 1 convolution operations, and bias addition/scale, performances are improved by 1.1 × –2.4 × . In addition, our technique reduces energy consumptions when performing image processing, 1 × 1 convolution, and bias addition/scale by 7.9–17.7%, 46.8–57.7%, and 41.7–58.5%, respectively.

Original languageEnglish
Article number102897
JournalMicroprocessors and Microsystems
Publication statusPublished - 2019 Nov

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2018R1D1A3B07045908 ).

Publisher Copyright:
© 2019 Elsevier B.V.


  • Accelerator
  • Cooperative data transfer
  • Direct memory access
  • Heterogeneous computing
  • Stream operation

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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