The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. We propose that the brain’s ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications.  

Project Features:

• Combines recent advances in stochastic circuits with neuromorphic algorithms for probabilistic applications 
• Aims to scale up probabilistic computing through neuromorphic architectures and algorithms. Success is to produce the right type of random numbers at the necessary quality and quantity of random numbers for probabilistic applications 

Desired Outcomes:

• Show Empirical and Theoretical Application Impact
• Formalize Theoretical Framework
• Prototype Probabilistic Neuromorphic System
• Execute a Scalable and Tunable Stochastic Device Strategy