  Integrated sensing and communication (ISAC) is a promising solution to
accelerate edge inference via the dual use of wireless signals. However, this
paradigm needs to minimize the inference error and latency under ISAC
co-functionality interference, for which the existing ISAC or edge resource
allocation algorithms become inefficient, as they ignore the inter-dependency
between low-level ISAC designs and high-level inference services. This letter
proposes an inference-oriented ISAC (IO-ISAC) scheme, which minimizes upper
bounds on end-to-end inference error and latency using multi-objective
optimization. The key to our approach is to derive a multi-view inference model
that accounts for both the number of observations and the angles of
observations, by integrating a half-voting fusion rule and an angle-aware
sensing model. Simulation results show that the proposed IO-ISAC outperforms
other benchmarks in terms of both accuracy and latency.
