  In 2016, Karney proposed an exact sampling algorithm for the standard normal
distribution. In this paper, we study the computational complexity of this
algorithm under the random deviate model. Specifically, Karney's algorithm
requires the access to an infinite sequence of independently and uniformly
random deviates over the range (0,1). We give an estimate of the expected
number of uniform deviates used by this algorithm until outputting a sample
value, and present an improved algorithm with lower uniform deviate
consumption. The experimental results also shows that our improved algorithm
has better performance than Karney's algorithm.
