Hardware Design for Autonomous Bayesian Networks

Abstract

Directed acyclic graphs or Bayesian networks that are popular in many AI related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits built out of probabilistic bits (p-bits), analogous to binary stochastic neurons of stochastic artificial neural networks. In order to satisfy standard statistical results, individual p-bits not only need to be updated sequentially, but also in order from the parent to the child nodes, necessitating the use of sequencers in software implementations. In this article, we first use SPICE simulations to show that an autonomous hardware Bayesian network can operate correctly without any clocks or sequencers, but only if the individual p-bits are appropriately designed. We then present a simple behavioral model of the autonomous hardware illustrating the essential characteristics needed for correct sequencer-free operation. This model is also benchmarked against SPICE simulations and can be used to simulate large scale networks. Our results could be useful in the design of hardware accelerators that use energy efficient building blocks suited for low-level implementations of Bayesian networks. The autonomous massively parallel operation of our proposed stochastic hardware has biological relevance since neural dynamics in brain is also stochastic and autonomous by nature.

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Cited by in corpus (9)

• A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
• Probabilistic computing with p-bits
• Hardware-aware $insitu$ Boltzmann machine learning using stochastic magnetic tunnel junctions
• Experimental demonstration of an integrated on-chip p-bit core utilizing stochastic Magnetic Tunnel Junctions and 2D-MoS2 FETs
• Spintronics-compatible approach to solving maximum satisfiability problems with probabilistic computing, invertible logic and parallel tempering
• Efficient Probabilistic Computing with Stochastic Perovskite Nickelates
• Accelerated Quantum Monte Carlo with Probabilistic Computers
• Application of Probabilistic-bit in Precision Measurements
• Ground-State Probabilistic Logic with the Simplest Binary Energy Landscape for Probabilistic Computing