  Learning at the edge is a challenging task from several perspectives, since
data must be collected by end devices (e.g. sensors), possibly pre-processed
(e.g. data compression), and finally processed remotely to output the result of
training and/or inference phases. This involves heterogeneous resources, such
as radio, computing and learning related parameters. In this context, we
propose an algorithm that dynamically selects data encoding scheme, local
computing resources, uplink radio parameters, and remote computing resources,
to perform a classification task with the minimum average end devices' energy
consumption, under E2E delay and inference reliability constraints. Our method
does not assume any prior knowledge of the statistics of time varying context
parameters, while it only requires the solution of low complexity per-slot
deterministic optimization problems, based on instantaneous observations of
these parameters and that of properly defined state variables. Numerical
results on convolutional neural network based image classification illustrate
the effectiveness of our method in striking the best trade-off between energy,
delay and inference reliability.
