TY - JOUR
T1 - Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP
AU - Qiu, Meikang
AU - Jia, Zhiping
AU - Xue, Chun
AU - Shao, Zili
AU - Sha, Edwin H.-M.
PY - 2007/1
Y1 - 2007/1
N2 - Dynamic Voltage Scaling (DVS) is one of the techniques used to obtain energy-saving in real-time DSP systems. In many DSP systems, some tasks contain conditional instructions that have different execution times for different inputs. Due to the uncertainties in execution time of these tasks, this paper models each varied execution time as a probabilistic random variable and solves the Voltage Assignment with Probability (VAP) Problem. VAP problem involves finding a voltage level to be used for each node of an date flow graph (DFG) in uniprocessor and multiprocessor DSP systems. This paper proposes two optimal algorithms, one for uniprocessor and one for multiprocessor DSP systems, to minimize the expected total energy consumption while satisfying the timing constraint with a guaranteed confidence probability. The experimental results show that our approach achieves significant energy saving than previous work. For example, our algorithm for multiprocessor achieves an average improvement of 56.1% on total energy-saving with 0.80 probability satisfying timing constraint. © Springer Science+Business Media, LLC 2007.
AB - Dynamic Voltage Scaling (DVS) is one of the techniques used to obtain energy-saving in real-time DSP systems. In many DSP systems, some tasks contain conditional instructions that have different execution times for different inputs. Due to the uncertainties in execution time of these tasks, this paper models each varied execution time as a probabilistic random variable and solves the Voltage Assignment with Probability (VAP) Problem. VAP problem involves finding a voltage level to be used for each node of an date flow graph (DFG) in uniprocessor and multiprocessor DSP systems. This paper proposes two optimal algorithms, one for uniprocessor and one for multiprocessor DSP systems, to minimize the expected total energy consumption while satisfying the timing constraint with a guaranteed confidence probability. The experimental results show that our approach achieves significant energy saving than previous work. For example, our algorithm for multiprocessor achieves an average improvement of 56.1% on total energy-saving with 0.80 probability satisfying timing constraint. © Springer Science+Business Media, LLC 2007.
KW - Assignment
KW - DSP
KW - DVS
KW - Probability
KW - Real-time
UR - http://www.scopus.com/inward/record.url?scp=33947287207&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33947287207&origin=recordpage
U2 - 10.1007/s11265-006-0002-0
DO - 10.1007/s11265-006-0002-0
M3 - RGC 22 - Publication in policy or professional journal
SN - 1387-5485
VL - 46
SP - 55
EP - 73
JO - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
JF - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
IS - 1
ER -