  Recently, the Edge Computing paradigm has gained significant popularity both
in industry and academia. Researchers now increasingly target to improve
performance and reduce energy consumption of such devices. Some recent efforts
focus on using emerging RRAM technologies for improving energy efficiency,
thanks to their no leakage property and high integration density. As the
complexity and dynamism of applications supported by such devices escalate, it
has become difficult to maintain ideal performance by static RRAM controllers.
Machine Learning provides a promising solution for this, and hence, this work
focuses on extending such controllers to allow dynamic parameter updates. In
this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which
periodically updates Wait Buffer and batch sizes using on-the-fly learning
models and gradient ascent. AVAC allows Edge devices to adapt to different
applications and their stages, to improve computation performance and reduce
energy consumption. Simulations demonstrate that the proposed model can provide
up to 29% increase in performance and 19% decrease in energy, compared to
static controllers, using traces of real-life healthcare applications on a
Raspberry-Pi based Edge deployment.
