Random error analysis and reduction for stochastic computation based on autocorrelation sequence
Cheng Ye, Jianhao Hu · 2014
This paper proposes the random error analysis method for stochastic computation based on autocorrelation sequence (AS), which is more general than the previous work based on Bernoulli sequence (BS). The analysis results show the use of proper ASs as input streams is able to reduce random error compared to the conventional use of BSs. In order to confirm that conclusion, we apply an AS, referred as Maximal Concentrated Autocorrelation Sequence (MCAS), into the stochastic computation system which implements Bernstein polynomial. Both the theoretical analysis and simulation results reveal that the use of MCAS reduces the random error.