  With the wide spread use of AI-driven systems in the edge (a.k.a edge
intelligence systems), such as autonomous driving vehicles, wearable biotech
devices, intelligent manufacturing, etc., such systems are becoming very
critical for our day-to-day lives. A challenge in designing edge intelligence
systems is that we have to deal with a large number of constraints in two
design spaces that form the basis of such systems: the edge design space and
the deep learning design space. Thus in this work, a new systematic,
extendable, manual approach, READLE, is proposed for creating representations
of specifications in edge intelligent systems, capturing constraints in the
edge system design space (e.g. timing constraints and other performance
constraints) and constraints in the deep learning space (e.g. model training
duration, required level of accuracy) in a coherent fashion. In particular,
READLE leverages benefits of real-time logic and binary decision diagrams to
generate unified specifications. Several insights learned in building READLE
are also discussed, which should help in future research in the domain of
formal specifications for edge intelligent systems.
