TY - GEN
T1 - Gaussian process regression for improving the performance of self-powered time-of-occurrence sensors
AU - Zhou, Liang
AU - Aono, Kenji
AU - Chakrabartty, Shantanu
N1 - Funding Information:
This work was supported in part by the National Science Foundation under Grant ECCS-1550096,CNS-1646380, DGE-0802267 and DGE-1143954.
Publisher Copyright:
© 2018 IEEE
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In our previous work, we had demonstrated a CMOS timer-injector integrated circuit for self-powered sensing of time-of-occurrence of mechanical events. While the sensor could achieve an improved time-stamping accuracy by averaging the output across over multiple channels, the mismatch between the channels made the calibration process cumbersome and time-consuming. In this paper, we propose the use of non-parametric machine learning techniques to achieve more robust and accurate event reconstruction. This is demonstrated using training and testing data that were obtained from fabricated prototypes on a 0.5-μm CMOS process; the model trained using Gaussian process regression can achieve an average recovery accuracy of 3.3% on testing data, which is comparable to the performance of using an averaging technique on calibrated injection results. The experimental results also validate that scalable performance can be achieved by employing more injection channels.
AB - In our previous work, we had demonstrated a CMOS timer-injector integrated circuit for self-powered sensing of time-of-occurrence of mechanical events. While the sensor could achieve an improved time-stamping accuracy by averaging the output across over multiple channels, the mismatch between the channels made the calibration process cumbersome and time-consuming. In this paper, we propose the use of non-parametric machine learning techniques to achieve more robust and accurate event reconstruction. This is demonstrated using training and testing data that were obtained from fabricated prototypes on a 0.5-μm CMOS process; the model trained using Gaussian process regression can achieve an average recovery accuracy of 3.3% on testing data, which is comparable to the performance of using an averaging technique on calibrated injection results. The experimental results also validate that scalable performance can be achieved by employing more injection channels.
UR - http://www.scopus.com/inward/record.url?scp=85062231197&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS.2018.8624046
DO - 10.1109/MWSCAS.2018.8624046
M3 - Conference contribution
AN - SCOPUS:85062231197
T3 - Midwest Symposium on Circuits and Systems
SP - 996
EP - 999
BT - 2018 IEEE 61st International Midwest Symposium on Circuits and Systems, MWSCAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018
Y2 - 5 August 2018 through 8 August 2018
ER -