TY - GEN
T1 - Information-theoretic limits of algorithmic noise tolerance
AU - Seo, Daewon
AU - Varshney, Lav R.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/8
Y1 - 2016/11/8
N2 - Statistical error compensation techniques in computing circuits are becoming prevalent, especially as implemented on nanoscale physical substrates. One such technique that has been developed and deployed is algorithmic noise tolerance (ANT), which aggregates information from several computational branches operating at different points along energy-reliability circuit tradeoffs. To understand this practical approach better, it is of interest to develop limit theorems on optimal designs, no matter how much design effort is put in. The purpose of this paper is to develop a fundamental limit for ANT by making an analogy to the CEO problem in multiterminal source coding, extended to the setting with a mixed set of discrete and continuous random variables. Since statistical signal processing and machine learning are key workloads for modern computing, we specifically discuss performance measured according to logarithmic distortion, in addition to mean-squared error. We find the Gaussian CEO problem provides performance bounds for ANT under both kinds of distortion.
AB - Statistical error compensation techniques in computing circuits are becoming prevalent, especially as implemented on nanoscale physical substrates. One such technique that has been developed and deployed is algorithmic noise tolerance (ANT), which aggregates information from several computational branches operating at different points along energy-reliability circuit tradeoffs. To understand this practical approach better, it is of interest to develop limit theorems on optimal designs, no matter how much design effort is put in. The purpose of this paper is to develop a fundamental limit for ANT by making an analogy to the CEO problem in multiterminal source coding, extended to the setting with a mixed set of discrete and continuous random variables. Since statistical signal processing and machine learning are key workloads for modern computing, we specifically discuss performance measured according to logarithmic distortion, in addition to mean-squared error. We find the Gaussian CEO problem provides performance bounds for ANT under both kinds of distortion.
UR - http://www.scopus.com/inward/record.url?scp=85006010303&partnerID=8YFLogxK
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U2 - 10.1109/ICRC.2016.7738715
DO - 10.1109/ICRC.2016.7738715
M3 - Conference contribution
AN - SCOPUS:85006010303
T3 - 2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - Conference Proceedings
BT - 2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Rebooting Computing, ICRC 2016
Y2 - 17 October 2016 through 19 October 2016
ER -