Selective Event Processing for Energy Efficient Mobile Gaming with SNIP

Abstract

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.