TY - GEN
T1 - CyHOP
T2 - 34th IEEE International Conference on Computer Design, ICCD 2016
AU - Fallahzadeh, Ramin
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/22
Y1 - 2016/11/22
N2 - Power consumption is a major obstacle in designing stringent resource constraint wearables. Several system-level design considerations contribute to energy consumption of these systems which must be taken into account while designing the system. We propose a power-performance optimization framework, namely CyHOP (Cyclic and Holistic Optimization framework), for connected wearable motion sensors. While existing work focus solely on one design parameter, our approach globally trades-off the performance of activity recognition and power consumption. CyHOP is capable of optimally adjusting the system to fulfill specific application needs. Using a smoothing technique, the initial multi-variate non-convex optimization problem is reduced to a convex problem and solved using our devised derivative-free optimization approach, namely, cyclic coordinate search. Our model performs a linear search by cycling through the system variables on each iteration until it converges to the global optimum. Using real-world data collected with wearable motion sensors during activity monitoring, we validate our approached with various performance thresholds ranging from 40% to 80%.
AB - Power consumption is a major obstacle in designing stringent resource constraint wearables. Several system-level design considerations contribute to energy consumption of these systems which must be taken into account while designing the system. We propose a power-performance optimization framework, namely CyHOP (Cyclic and Holistic Optimization framework), for connected wearable motion sensors. While existing work focus solely on one design parameter, our approach globally trades-off the performance of activity recognition and power consumption. CyHOP is capable of optimally adjusting the system to fulfill specific application needs. Using a smoothing technique, the initial multi-variate non-convex optimization problem is reduced to a convex problem and solved using our devised derivative-free optimization approach, namely, cyclic coordinate search. Our model performs a linear search by cycling through the system variables on each iteration until it converges to the global optimum. Using real-world data collected with wearable motion sensors during activity monitoring, we validate our approached with various performance thresholds ranging from 40% to 80%.
UR - http://www.scopus.com/inward/record.url?scp=85006817430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006817430&partnerID=8YFLogxK
U2 - 10.1109/ICCD.2016.7753320
DO - 10.1109/ICCD.2016.7753320
M3 - Conference contribution
AN - SCOPUS:85006817430
T3 - Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016
SP - 428
EP - 431
BT - Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 October 2016 through 5 October 2016
ER -