  Time-series data processing is an important component of many real-world
applications, such as health monitoring, environmental monitoring, and digital
agriculture. These applications collect distinct windows of sensor data (e.g.,
few seconds) and process them to assess the environment. Machine learning (ML)
models are being employed in time-series applications due to their
generalization abilities for classification. State-of-the-art time-series
applications wait for entire sensor data window to become available before
processing the data using ML algorithms, resulting in high sensor energy
consumption. However, not all situations require processing full sensor window
to make accurate inference. For instance, in activity recognition, sitting and
standing activities can be inferred with partial windows. Using this insight,
we propose to employ early exit classifiers with partial sensor windows to
minimize energy consumption while maintaining accuracy. Specifically, we first
utilize multiple early exits with successively increasing amount of data as
they become available in a window. If early exits provide inference with high
confidence, we return the label and enter low power mode for sensors. The
proposed approach has potential to enable significant energy savings in time
series applications. We utilize neural networks and random forest classifiers
to evaluate our approach. Our evaluations with six datasets show that the
proposed approach enables up to 50-60% energy savings on average without any
impact on accuracy. The energy savings can enable time-series applications in
remote locations with limited energy availability.
