  Based on the observation that application phases exhibit varying degrees of
sensitivity to noise (i.e., accuracy loss) in computation during execution,
this paper explores how Dynamic Precision Scaling (DPS) can maximize power
efficiency by tailoring the precision of computation adaptively to temporal
changes in algorithmic noise tolerance. DPS can decrease the arithmetic
precision of noise-tolerant phases to result in power savings at the same
operating speed (or faster execution within the same power budget), while
keeping the overall loss in accuracy due to precision reduction bounded.
