Realization of Stochastic Neural Networks and Its Potential Applications
This work addresses a domain-specific problem in signal processing or communications, but it appears incremental as it builds on existing SC decoder methods without claiming major breakthroughs.
The paper tackles the challenge of improving Successive Cancellation Decoders by exploring stochastic neural networks as a solution, aiming to enhance their performance and practicality, though no concrete results or numbers are provided.
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.