View : 596 Download: 0
Energy effective data migration methodology with memory access awareness for IoT devices
- Title
- Energy effective data migration methodology with memory access awareness for IoT devices
- Authors
- Han Y.; Park S.
- Ewha Authors
- 박상수
- SCOPUS Author ID
- 박상수
- Issue Date
- 2018
- Journal Title
- 1st International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI-NCON 2018
- Citation
- 1st International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI-NCON 2018, pp. 54 - 59
- Keywords
- Battery life; Data migration; Embedded system; Energy efficiency; Hybrid memory; Internet of Things
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Indexed
- SCOPUS
- Document Type
- Conference Paper
- Abstract
- The rapid development of Internet of Things (IoT) technology has led to the appearance of many IoT devices in various industries, such as the home appliance and healthcare device industries. Most IoT devices are becoming minimized and are battery-operated because of the mobility of these devices. Thus, reducing the execution time and energy consumption has become an important problem, as has the need to extend the battery life. In this paper, we propose a data migration methodology that transfers read-dominant data from SRAM to the flash memory with the aim of improving the performance of small-scale embedded systems. We trace the memory access in the hybrid memory and analyze the memory access patterns to separate the read-dominant data from among the read/write data. The read-dominant data are then relocated to the flash memory sector. These procedures enabled us to reduce the energy consumption for accessing the data in SRAM. Experiments showed that, compared with placing data in SRAM, the proposed methodology achieved an improvement in the execution time, energy consumption, and battery life. © 2018 IEEE.
- DOI
- 10.1109/ECTI-NCON.2018.8378281
- ISBN
- 9781538617342
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML