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Muller C-Element Exploiting Programmable Metallization Cell for Bayesian Inference
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DOI：10.1109/JETCAS.2022.3206479.png)
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Abstract
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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-Ge0.3Se0.7 PMC and CMOS inverter (in 7 nm technology node), we show that the proposed Muller C-element is extremely compact and consumes at least similar to 9x 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.
Keywords:
Bayesian inference
Muller C-element
programmable metallization cell
resistive RAM
stochastic computing
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3.8
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1.4K
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2.8K
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