Muller C-Element Exploiting Programmable Metallization Cell for Bayesian Inference

Abstract

Decision-making via Bayesian inference is a prominent operation in several autonomous applications, including robotics, brain-machine interactions, artificial intelligence (AI) agents, etc. A cascaded tree of asynchronous logic elements, known as Muller C-elements, can perform stochastic Bayesian inference efficiently. Therefore, there is an urgent need for compact and energy-efficient Muller C-element realizations. To this end, in this work, for the first time, we propose a Muller C-element utilizing a Programmable Metallization Cell (PMC) and a CMOS inverter. Using an experimentally calibrated, in-house developed physics-based compact model of Ag-Ge0.3Se 0.7 PMC and CMOS inverter (in 7 nm technology node), we show that the proposed Muller C-element is extremely compact and consumes at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 9\times $ </tex-math></inline-formula> less power compared to the previously reported implementations. Furthermore, we demonstrate that the proposed PMC-based Muller C-element can make Bayesian inferences on bitstream-encoded data with reasonable accuracy. Our results indicate that the computational precision depends significantly on the number of output bits and the probability encoded by the inputs to the Muller C-element. Moreover, we also propose a novel readout circuit design to facilitate cascading of multiple PMC-based Muller C-elements to increase the number of evidence sources and demonstrate its efficacy for spam filtering applications.