  Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile
robots have shown huge potential for improving human productivity. These mobile
agents require low power/energy consumption to have a long lifespan since they
are usually powered by batteries. These agents also need to adapt to
changing/dynamic environments, especially when deployed in far or dangerous
locations, thus requiring efficient online learning capabilities. These
requirements can be fulfilled by employing Spiking Neural Networks (SNNs) since
SNNs offer low power/energy consumption due to sparse computations and
efficient online learning due to bio-inspired learning mechanisms. However, a
methodology is still required to employ appropriate SNN models on autonomous
mobile agents. Towards this, we propose a Mantis methodology to systematically
employ SNNs on autonomous mobile agents to enable energy-efficient processing
and adaptive capabilities in dynamic environments. The key ideas of our Mantis
include the optimization of SNN operations, the employment of a bio-plausible
online learning mechanism, and the SNN model selection. The experimental
results demonstrate that our methodology maintains high accuracy with a
significantly smaller memory footprint and energy consumption (i.e., 3.32x
memory reduction and 2.9x energy saving for an SNN model with 8-bit weights)
compared to the baseline network with 32-bit weights. In this manner, our
Mantis enables the employment of SNNs for resource- and energy-constrained
mobile agents.
