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Memory streaming acceleration for embedded systems with CPU-accelerator cooperative data processing.
- Source :
-
Microprocessors & Microsystems . Nov2019, Vol. 71, pN.PAG-N.PAG. 1p. - Publication Year :
- 2019
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01419331
- Volume :
- 71
- Database :
- Academic Search Index
- Journal :
- Microprocessors & Microsystems
- Publication Type :
- Academic Journal
- Accession number :
- 139454825
- Full Text :
- https://doi.org/10.1016/j.micpro.2019.102897