Realization of Stochastic Neural Networks and Its Potential Applications

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.

Principles of Stochastic Computing: Fundamental Concepts and Applications

The semiconductor and IC industry is facing the issue of high energy consumption. In modern days computers and processing systems are designed based on the Turing machine and Von Neumann's architecture. This architecture mainly focused on designing systems based on deterministic behaviors. To tackle energy consumption and reliability in systems, Stochastic Computing was introduced. In this research, we aim to review and study the principles behind stochastic computing and its implementation techniques. By utilizing stochastic computing, we can achieve higher energy efficiency and smaller area sizes in terms of designing arithmetic units. Also, we aim to popularize the affiliation of Stochastic systems in designing futuristic BLSI and Neuromorphic systems.