Successive Cancellation Decoders have come a long way since the implementation of traditional SC decoders, but there still is a potential for improvement. The main struggle over the years was to find an optimal algorithm to implement them. Most of the proposed algorithms are not practical enough to be implemented in real-life. In this research, we aim to introduce the Efficiency of stochastic neural networks as an SC decoder and Find the possible ways of improving its performance and practicality. In this paper, after a brief introduction to stochastic neurons and SNNs, we introduce methods to realize Stochastic NNs on both deterministic and stochastic platforms.

中文翻译：

---

随机神经网络的实现及其潜在应用

自实施传统 SC 解码器以来，连续取消解码器已经取得了长足的进步，但仍有改进的潜力。多年来的主要斗争是找到实现它们的最佳算法。大多数提出的算法不够实用，无法在现实生活中实现。在这项研究中，我们旨在介绍随机神经网络作为 SC 解码器的效率，并寻找提高其性能和实用性的可能方法。在本文中，在简要介绍了随机神经元和 SNN 之后，我们介绍了在确定性和随机平台上实现随机 NN 的方法。