TY - GEN
T1 - Selective Event Processing for Energy Efficient Mobile Gaming with SNIP
AU - Rengasamy, Prasanna Venkatesh
AU - Zhang, Haibo
AU - Zhao, Shulin
AU - Sivasubramaniam, Anand
AU - Kandemir, Mahmut T.
AU - Das, Chita R.
N1 - Funding Information:
This research is supported in part by NSF grants 1763681, 1629915, 1629129, 1317560, 1526750, 1714389, 1912495, and a DARPA/SRC JUMP grant.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Gaming is an important class of workloads for mobile devices. They are not only one of the biggest markets for game developers and app stores, but also amongst the most stressful applications for the SoC. In these workloads, much of the computation is user-driven, i.e. events captured from sensors drive the computation to be performed. Consequently, event processing constitutes the bulk of energy drain for these applications. To address this problem, we conduct a detailed characterization of event processing activities in several popular games and show that (i) some of the events are exactly repetitive in their inputs, not requiring any processing at all; or (ii) a significant number of events are redundant in that even if the inputs for these events are different, the output matches events already processed. Memoization is one of the obvious choices to optimize such behavior, however the problem is a lot more challenging in this context because the computation can span even functional/OS boundaries, and the input space required for tables can takes gigabytes of storage. Instead, our Selecting Necessary InPuts (SNIP) software solution uses machine learning to isolate the input features that we really need to track in order to considerably shrink memoization tables. We show that SNIP can save up to 32% of the energy in these games without requiring any hardware modifications.
AB - Gaming is an important class of workloads for mobile devices. They are not only one of the biggest markets for game developers and app stores, but also amongst the most stressful applications for the SoC. In these workloads, much of the computation is user-driven, i.e. events captured from sensors drive the computation to be performed. Consequently, event processing constitutes the bulk of energy drain for these applications. To address this problem, we conduct a detailed characterization of event processing activities in several popular games and show that (i) some of the events are exactly repetitive in their inputs, not requiring any processing at all; or (ii) a significant number of events are redundant in that even if the inputs for these events are different, the output matches events already processed. Memoization is one of the obvious choices to optimize such behavior, however the problem is a lot more challenging in this context because the computation can span even functional/OS boundaries, and the input space required for tables can takes gigabytes of storage. Instead, our Selecting Necessary InPuts (SNIP) software solution uses machine learning to isolate the input features that we really need to track in order to considerably shrink memoization tables. We show that SNIP can save up to 32% of the energy in these games without requiring any hardware modifications.
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U2 - 10.1109/IISWC50251.2020.00035
DO - 10.1109/IISWC50251.2020.00035
M3 - Conference contribution
AN - SCOPUS:85097809886
T3 - Proceedings - 2020 IEEE International Symposium on Workload Characterization, IISWC 2020
SP - 288
EP - 299
BT - Proceedings - 2020 IEEE International Symposium on Workload Characterization, IISWC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Symposium on Workload Characterization, IISWC 2020
Y2 - 27 October 2020 through 29 October 2020
ER -