  This paper addresses the challenging problem of energy-efficient and
uncertainty-aware pose estimation in insect-scale drones, which is crucial for
tasks such as surveillance in constricted spaces and for enabling non-intrusive
spatial intelligence in smart homes. Since tiny drones operate in highly
dynamic environments, where factors like lighting and human movement impact
their predictive accuracy, it is crucial to deploy uncertainty-aware prediction
algorithms that can account for environmental variations and express not only
the prediction but also confidence in the prediction. We address both of these
challenges with Compute-in-Memory (CIM) which has become a pivotal technology
for deep learning acceleration at the edge. While traditional CIM techniques
are promising for energy-efficient deep learning, to bring in the robustness of
uncertainty-aware predictions at the edge, we introduce a suite of novel
techniques: First, we discuss CIM-based acceleration of Bayesian filtering
methods uniquely by leveraging the Gaussian-like switching current of CMOS
inverters along with co-design of kernel functions to operate with extreme
parallelism and with extreme energy efficiency. Secondly, we discuss the
CIM-based acceleration of variational inference of deep learning models through
probabilistic processing while unfolding iterative computations of the method
with a compute reuse strategy to significantly minimize the workload. Overall,
our co-design methodologies demonstrate the potential of CIM to improve the
processing efficiency of uncertainty-aware algorithms by orders of magnitude,
thereby enabling edge robotics to access the robustness of sophisticated
prediction frameworks within their extremely stringent area/power resources.
