论文标题
实现随机神经网络及其潜在应用
Realization of Stochastic Neural Networks and Its Potential Applications
论文作者
论文摘要
自实施传统SC解码器以来，连续的取消解码器已经走了很长一段路，但是仍然有改进的潜力。多年来，主要的斗争是找到一种实施它们的最佳算法。大多数提出的算法不够实用，无法在现实生活中实施。在这项研究中，我们旨在介绍作为SC解码器的随机神经网络的效率，并找到改善其性能和实用性的可能方法。在本文中，在对随机神经元和SNN进行了简要介绍之后，我们介绍了在确定性和随机平台上实现随机NN的方法。
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.
