Ferroelectric FET-Based Bayesian Inference Engine for Disease Diagnosis

Probabilistic/stochastic computations form the backbone of autonomous systems and classifiers. Recently, biomedical applications of probabilistic computing such as Bayesian networks for disease diagnosis, DNA sequencing, etc. have attracted significant attention owing to their high energy-efficiency. Bayesian inference is widely used for decision making based on independent (often conflicting) sources of information/evidence. A cascaded chain or tree structure of asynchronous circuit elements known as Muller C-elements can effectively implement Bayesian inference. Such circuits utilize stochastic bit streams to encode input probabilities which enhances their robustness and fault-tolerance. However, the CMOS implementations of Muller C-element are bulky and energy hungry which restricts their widespread application in resource constrained IoT and mobile devices such as UAVs, robots, space rovers, etc. In this work, for the first time, we propose a compact and energy-efficient implementation of Muller C-element utilizing a single Ferroelectric FET and use it for cancer diagnosis task by performing Bayesian inference with high accuracy on Wisconsin data set. The proposed implementation exploits the unique drain-erase, program inhibit and drain-erase inhibit characteristics of FeFETs to yield the output as the polarization-state of the ferroelectric layer. Our extensive investigation utilizing an in-house developed experimentally calibrated compact model of FeFET reveals that the proposed C-element consumes (worst-case) energy of 4.1 fJ and an area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.07~\mu m^{2}$ </tex-math></inline-formula> and outperforms the prior implementations in terms of energy-efficiency and footprint while exhibiting a comparable delay. We also propose a novel read circuitry for realising a Bayesian inference engine by cascading a network of proposed FeFET-based C-elements for practical applications. Furthermore, for the first time, we analyze the impact of cross-correlation between the stochastic input bit streams on the accuracy of the C-element based Bayesian inference implementation.