A critical challenge for modern system design is meeting the overwhelming performance, storage, and communication bandwidth demand of emerging applications within a tightly bound power budget. As both the time and power, hence the energy, spent in data communication by far exceeds the energy spent in actual data generation (i.e., computation), (re)computing data can easily become cheaper than storing and retrieving (pre)computed data. Therefore, trading computation for communication can improve energy efficiency by minimizing the energy overhead incurred by data storage, retrieval, and communication. This paper provides a taxonomy for the computation versus communication trade-off accompanied by a quantitative characterization.
Bibliographical noteFunding Information:
This work was supported by US National Science Foundation CAREER CCF-1553042.
© 2013 IEEE.
- Data recomputation
- amnesic execution
- communication reduction
- energy efficiency
- load value prediction