Dither computing：一个混合确定性-随机计算框架
Dither computing: a hybrid deterministic-stochastic computing framework
摘要：
随机计算作为在计算机上执行算术的替代方法有着悠久的历史。虽然它可以被认为是实数的无偏估计量，但它的方差和MSE为$Ω(\frac{1}{N})$的量级。另一方面，随机计算的确定性变体去除了随机方面，但不能以任意精度近似任意实数，并且是有偏估计。然而，它们具有$O(\frac{1}{N^2})$数量级的渐近优MSE。最近在随机舍入的深度学习中的结果表明，舍入中的偏差会降低性能。我们提出了一个替代框架，称为抖动计算，结合随机计算和其确定性变量的方面，可以执行类似的效率计算，是无偏的，方差和MSE也对$Θ(\frac{1}{N^2})$的最佳顺序。我们还表明，它可以是有益的随机舍入应用程序以及。我们提供的实施细节，并给出实验结果比较显示所提出的计划的好处。
英文摘要：
Stochastic computing has a long history as an alternative method of performing arithmetic on a computer. While it can be considered an unbiased estimator of real numbers, it has a variance and MSE on the order of $Ω(\frac{1}{N})$. On the other hand, deterministic variants of stochastic computing remove the stochastic aspect, but cannot approximate arbitrary real numbers with arbitrary precision and are biased estimators. However, they have an asymptotically superior MSE on the order of $O(\frac{1}{N^2})$. Recent results in deep learning with stochastic rounding suggest that the bias in the rounding can degrade performance. We proposed an alternative framework, called dither computing, that combines aspects of stochastic computing and its deterministic variants and that can perform computing with similar efficiency, is unbiased, and with a variance and MSE also on the optimal order of $Θ(\frac{1}{N^2})$. We also show that it can be beneficial in stochastic rounding applications as well. We provide implementation details and give experimental results to comparatively show the benefits of the proposed scheme.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
