Stochastic Computing and Its Application

Dear Colleagues,

Stochastic computing is a paradigm that leverages the principles of randomness and probability to perform complex computations using simple hardware. Unlike traditional binary computing, which relies on deterministic logic operations, stochastic computing represents data and performs computations probabilistically. This method can significantly reduce hardware complexity and power consumption, making it particularly suitable for applications in energy-constrained environments. The roots of stochastic computing can be traced back to the 1960s, but recent advancements in technology and a growing interest in low-power and resilient computing have reignited research in this area. Modern applications, including machine learning, neural networks, image processing, and signal processing, have demonstrated the potential benefits of stochastic computing. With the advent of new materials, innovative circuit designs, and sophisticated algorithms, stochastic computing is poised to play a critical role in the future of computing technology.

This Special Issue highlights innovative approaches, theoretical developments, and practical implementations that leverage stochastic computing techniques. By showcasing cutting-edge research and real-world applications, we aim to foster a deeper understanding of stochastic computing's potential and inspire future advancements.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Theoretical Foundations: Innovations in stochastic computing models, algorithms, and mathematical frameworks.
2. Hardware Design: Advances in stochastic circuit design, hardware implementations, and integration with existing technologies.
3. Energy-Efficient Computing: Applications and methodologies for reducing power consumption using stochastic computing techniques.
4. Fault Tolerance and Robustness: Strategies for enhancing fault tolerance and robustness in stochastic computing systems.
5. Applications in Machine Learning: Exploration of stochastic computing techniques in machine learning and artificial intelligence, including neural networks and probabilistic models.
6. Signal Processing: Applications of stochastic computing in signal processing, including noise reduction and data reconstruction.
7. Neuromorphic Computing: Using stochastic principles in neuromorphic systems and brain-inspired computing architectures.
8. Approximate Computing: Investigating stochastic methods in approximate computing and their impact on performance and reliability.

We look forward to receiving your contributions.

Prof. Dr. Hongge Li
Dr. Kaining Han
Guest Editors

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• stochastic computing
• probabilistic algorithms
• machine learning
• signal processing
• hardware design
• power efficiency
• error tolerance