Abstract
Probabilistic/stochastic computations form the backbone of autonomous
systems and classifiers. Recently, biomedical applications of
probabilistic computing such as hyperdimensional computing for DNA
sequencing, Bayesian networks for disease diagnosis, 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
implementation of Muller C-Element are bulky and energy hungry which
restricts their widespread application in resource constrained IoT and
mobile devices. To enable Bayesian inference based decision making in
IoT devices such as UAVs, robots, space rovers, etc, for the first time,
we propose a highly compact and energy-efficient implementation of
Muller C-Element utilizing a single Ferroelectric FET. The proposed
implementation exploits the unique drain-erase, program inhibit and
drain inhibit characteristics of FeFETs to encode 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 an
ultra-low power of 1.07 fW. 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 implementations. For proof of concept demonstration,
we utilize the proposed FeFET based Muller C-Element for performing
breast cancer diagnosis utilizing Wisconsin data-set.