Probabilistic artificial neural networks offer intriguing prospects for enabling the uncertainty of artificial intelligence methods to be described explicitly in their function; however, the development of techniques that quantify uncertainty by well-understood methods such as Monte Carlo sampling has been limited by the high costs of stochastic sampling on deterministic computing hardware. Emerging computing systems that are amenable to hardware-level probabilistic computing, such as those that leverage stochastic devices, may make probabilistic neural networks more feasible in the not-too-distant future. This paper describes the scANN technique -- \textit{sampling (by coinflips) artificial neural networks} -- which enables neural networks to be sampled directly by treating the weights as Bernoulli coin flips. This method is natively well suited for probabilistic computing techniques that focus on tunable stochastic devices, nearly matches fully deterministic performance while also describing the uncertainty of correct and incorrect neural network outputs.

翻译：概率人工神经网络为明确描述人工智能方法的不确定性提供了诱人前景；然而，通过蒙特卡洛采样等成熟方法量化不确定性的技术发展，一直受限于确定性计算硬件上随机采样的高成本。新兴的适用于硬件级概率计算的计算系统（例如利用随机器件的系统）可能使概率神经网络在不久的将来变得更为可行。本文描述了scANN技术——（通过抛硬币方式）采样人工神经网络——该技术通过将权重视为伯努利抛硬币结果，实现对神经网络的直接采样。该方法天然适用于专注于可调随机器件的概率计算技术，其性能几乎与完全确定性方法相当，同时还能描述正确与错误神经网络输出的不确定性。