Probabilistic machine intelligence paradigms such as Bayesian Networks (BNs) are widely used in critical real-world applications. However they cannot be employed efficiently for large problems on conventional computing systems due to inefficiencies resulting from layers of abstraction and separation of logic and memory. We present an unconventional nanoscale magneto-electric machineparadigm, architected with the principle of physical equivalence to efficiently implement causal inference in BNs. It leverages emerging straintronic magneto-tunneling junctions in a novel mixed-signal circuit framework for direct computations on probabilities, while blurring the boundary between memory and computation.
Initial evaluations, based on extensive bottom-up simulations, indicate up to four orders of magnitude inference runtime speedup vs. best-case performance of 100-core microprocessors, for BNs with a million random variables. These could be the target applications for emerging magneto-electric devices to enable capabilities for leapfrogging beyond present day computing.