  The demand for explainable and energy-efficient artificial intelligence (AI)
systems for edge computing has led to significant interest in electronic
systems dedicated to Bayesian inference. Traditional designs of such systems
often rely on stochastic computing, which offers high energy efficiency but
suffers from latency issues and struggles with low-probability values. In this
paper, we introduce the logarithmic memristor-based Bayesian machine, an
innovative design that leverages the unique properties of memristors and
logarithmic computing as an alternative to stochastic computing. We present a
prototype machine fabricated in a hybrid CMOS/hafnium-oxide memristor process.
We validate the versatility and robustness of our system through experimental
validation and extensive simulations in two distinct applications: gesture
recognition and sleep stage classification. The logarithmic approach simplifies
the computational model by converting multiplications into additions and
enhances the handling of low-probability events, which are crucial in
time-dependent tasks. Our results demonstrate that the logarithmic Bayesian
machine achieves superior performance in terms of accuracy and energy
efficiency compared to its stochastic counterpart, particularly in scenarios
involving complex probabilistic models. This work paves the way for the
deployment of advanced AI capabilities in edge devices, where power efficiency
and reliability are paramount.
