  This paper is motivated by a simple question: Can we design and build
battery-free devices capable of machine learning and inference in underwater
environments? An affirmative answer to this question would have significant
implications for a new generation of underwater sensing and monitoring
applications for environmental monitoring, scientific exploration, and
climate/weather prediction.
  To answer this question, we explore the feasibility of bridging advances from
the past decade in two fields: battery-free networking and low-power machine
learning. Our exploration demonstrates that it is indeed possible to enable
battery-free inference in underwater environments. We designed a device that
can harvest energy from underwater sound, power up an ultra-low-power
microcontroller and on-board sensor, perform local inference on sensed
measurements using a lightweight Deep Neural Network, and communicate the
inference result via backscatter to a receiver. We tested our prototype in an
emulated marine bioacoustics application, demonstrating the potential to
recognize underwater animal sounds without batteries. Through this exploration,
we highlight the challenges and opportunities for making underwater
battery-free inference and machine learning ubiquitous.
