Elastic Fidelity: Trading-off Computational Accuracy for Energy   Reduction
  Power dissipation and energy consumption have become one of the most important problems in the design of processors today. This is especially true in power-constrained environments, such as embedded and mobile computing. While lowering the operational voltage can reduce power consumption, there are limits imposed at design time, beyond which hardware components experience faulty operation. Moreover, the decrease in feature size has led to higher susceptibility to process variations, leading to reliability issues and lowering yield. However, not all computations and all data in a workload need to maintain 100% fidelity. In this paper, we explore the idea of employing functional or storage units that let go the conservative guardbands imposed on the design to guarantee reliable execution. Rather, these units exhibit Elastic Fidelity, by judiciously lowering the voltage to trade-off reliable execution for power consumption based on the error guarantees required by the executing code. By estimating the accuracy required by each computational segment of a workload, and steering each computation to different functional and storage units, Elastic Fidelity Computing obtains power and energy savings while reaching the reliability targets required by each computational segment. Our preliminary results indicate that even with conservative estimates, Elastic Fidelity can reduce the power and energy consumption of a processor by 11-13% when executing applications involving human perception that are typically included in modern mobile platforms, such as audio, image, and video decoding. 
Sourya Roy, Tyler Clemons, S M Faisal, Ke Liu, Nikos Hardavellas,   Srinivasan Parthasarathy
cs.AR