TY - GEN
T1 - Segment-aware energy-efficient management of heterogeneous memory system for ultra-low-power IoT devices
AU - Choi, Hayeon
AU - Koo, Youngkyoung
AU - Park, Sangsoo
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea funded by the Korean Government (NRF-2014S1A5B6037290)
Publisher Copyright:
© 2017 Univeristy of Split, FESB.
PY - 2017/8/28
Y1 - 2017/8/28
N2 - The emergence of IoT (Internet of Things) has brought various studies on low-power techniques back to embedded systems. In general, minimizing power consumed by program executions is the main consideration of system design. While running programs, executing data-dependent program code results in a large number of memory accesses which consume huge amount of power. Further, most embedded systems consist of multiple types of memory devices, i.e., heterogeneous memory system, to benefit the different characteristics of memory devices. In this paper, we conduct a research on low-power techniques to reduce the power consumption of heterogeneous memory to achieve ultra-low-power in the system level. This study proposes a segment-aware energy-efficient management to improve the power efficiency considering the characteristics and structures of the memory devices. In the proposed approach, the technique migrates program code from allocated memory device to another if the consumed power is considered to be less. We also analyze and evaluate the comprehensive effects on energy efficiency by applying the technique as well. Compared to the unmodified program code, our model reduces power consumption up to 12.98% by migrating functions.
AB - The emergence of IoT (Internet of Things) has brought various studies on low-power techniques back to embedded systems. In general, minimizing power consumed by program executions is the main consideration of system design. While running programs, executing data-dependent program code results in a large number of memory accesses which consume huge amount of power. Further, most embedded systems consist of multiple types of memory devices, i.e., heterogeneous memory system, to benefit the different characteristics of memory devices. In this paper, we conduct a research on low-power techniques to reduce the power consumption of heterogeneous memory to achieve ultra-low-power in the system level. This study proposes a segment-aware energy-efficient management to improve the power efficiency considering the characteristics and structures of the memory devices. In the proposed approach, the technique migrates program code from allocated memory device to another if the consumed power is considered to be less. We also analyze and evaluate the comprehensive effects on energy efficiency by applying the technique as well. Compared to the unmodified program code, our model reduces power consumption up to 12.98% by migrating functions.
KW - Heterogeneous memory
KW - Internet of Things
KW - Power consumption
KW - Segment
KW - Ultra-Low-Power
UR - http://www.scopus.com/inward/record.url?scp=85030862892&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85030862892
T3 - 2017 2nd International Multidisciplinary Conference on Computer and Energy Science, SpliTech 2017
BT - 2017 2nd International Multidisciplinary Conference on Computer and Energy Science, SpliTech 2017
A2 - Rodrigues, Joel J. P. C.
A2 - Nizetic, Sandro
A2 - Patrono, Luigi
A2 - Rodrigues, Joel J. P. C.
A2 - Milanovic, Zeljka
A2 - Solic, Petar
A2 - Vukojevic, Katarina
A2 - Perkovic, Toni
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Multidisciplinary Conference on Computer and Energy Science, SpliTech 2017
Y2 - 12 July 2017 through 14 July 2017
ER -
