COINFLIPS – CO-designed Improved Neural Foundations Leveraging Inherent Physics Stochasticity – is a DOE Office of Science Co-Design in Microelectronics project that aims to develop a novel computing paradigm for probabilistic computing that leverages stochastic devices and brain-inspired architecture and algorithm principles.

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. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications.  We propose that the brain’s ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications.  To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures.

In COINFLIPS, we are exploring a co-design vision by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks.  

COINFLIPS has partners at New York University, University of Texas at Austin, University of Tennessee, Oak Ridge National Laboratory, and Temple University.

Project Website

Cognitive & Emerging Computing
Focus Areas
Cognitive Science
Computer Architecture
Aimone, James Bradley,


  • artificial intelligence
  • neural networks
  • neuromorphic
  • probabilistic
  • stochastic