  Realizing edge intelligence consists of sensing, communication, training, and
inference stages. Conventionally, the sensing and communication stages are
executed sequentially, which results in excessive amount of dataset generation
and uploading time. This paper proposes to accelerate edge intelligence via
integrated sensing and communication (ISAC). As such, the sensing and
communication stages are merged so as to make the best use of the wireless
signals for the dual purpose of dataset generation and uploading. However, ISAC
also introduces additional interference between sensing and communication
functionalities. To address this challenge, this paper proposes a
classification error minimization formulation to design the ISAC beamforming
and time allocation. The globally optimal solution is derived via the rank-1
guaranteed semidefinite relaxation, and performance analysis is performed to
quantify the ISAC gain over that of conventional edge intelligence. Simulation
results are provided to verify the effectiveness of the proposed ISAC-assisted
edge intelligence system. Interestingly, we find that ISAC is always
beneficial, when the duration of generating a sample is more than the duration
of uploading a sample. Otherwise, the ISAC gain can vanish or even be negative.
Nevertheless, we still derive a sufficient condition, under which a positive
ISAC gain is feasible.
