Implementing Bayesian Networks with Embedded Stochastic MRAM

Abstract

Magnetic tunnel junctions (MTJ's) with low barrier magnets have been used to implement random number generators (RNG's) and it has recently been shown that such an MTJ connected to the drain of a conventional transistor provides a three-terminal tunable RNG or a $p$-bit. In this letter we show how this $p$-bit can be used to build a $p$-circuit that emulates a Bayesian network (BN), such that the correlations in real world variables can be obtained from electrical measurements on the corresponding circuit nodes. The $p$-circuit design proceeds in two steps: the BN is first translated into a behavioral model, called Probabilistic Spin Logic (PSL), defined by dimensionless biasing (h) and interconnection (J) coefficients, which are then translated into electronic circuit elements. As a benchmark example, we mimic a family tree of three generations and show that the genetic relatedness calculated from a SPICE-compatible circuit simulator matches well-known results.

References in corpus (7)

• Stochastic p-bits for Invertible Logic
• Intrinsic optimization using stochastic nanomagnets
• Hardware emulation of stochastic p-bits for invertible logic
• Low Barrier Nanomagnets as p-bits for Spin Logic
• Weighted p-bits for FPGA implementation of probabilistic circuits
• A building block for hardware belief networks
• Voltage-driven Building Block for Hardware Belief Networks

Cited by in corpus (17)

• Massively Parallel Probabilistic Computing with Sparse Ising Machines
• Probabilistic computing with p-bits
• Hardware-aware $insitu$ Boltzmann machine learning using stochastic magnetic tunnel junctions
• Energy-efficient stochastic computing with superparamagnetic tunnel junctions
• Scalable Emulation of Sign-Problem$-$Free Hamiltonians with Room Temperature p-bits
• Experimental demonstration of an integrated on-chip p-bit core utilizing stochastic Magnetic Tunnel Junctions and 2D-MoS2 FETs
• Experimental Demonstration of Probabilistic Spin Logic by Magnetic Tunnel Junctions
• Quantitative Evaluation of Hardware Binary Stochastic Neurons
• All-Spin Bayesian Neural Networks
• Correlated fluctuations in spin orbit torque-coupled perpendicular nanomagnets
• Hardware Design for Autonomous Bayesian Networks
• Probabilistic-Bits based on Ferroelectric Field-Effect Transistors for Stochastic Computing
• Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire
• FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing
• In-situ learning harnessing intrinsic resistive memory variability through Markov Chain Monte Carlo Sampling
• Hardware implementation of Bayesian network building blocks with stochastic spintronic devices
• Ground-State Probabilistic Logic with the Simplest Binary Energy Landscape for Probabilistic Computing