  This paper proposes a neural architecture search (NAS) method for split
computing. Split computing is an emerging machine-learning inference technique
that addresses the privacy and latency challenges of deploying deep learning in
IoT systems. In split computing, neural network models are separated and
cooperatively processed using edge servers and IoT devices via networks. Thus,
the architecture of the neural network model significantly impacts the
communication payload size, model accuracy, and computational load. In this
paper, we address the challenge of optimizing neural network architecture for
split computing. To this end, we proposed NASC, which jointly explores optimal
model architecture and a split point to achieve higher accuracy while meeting
latency requirements (i.e., smaller total latency of computation and
communication than a certain threshold). NASC employs a one-shot NAS that does
not require repeating model training for a computationally efficient
architecture search. Our performance evaluation using hardware (HW)-NAS-Bench
of benchmark data demonstrates that the proposed NASC can improve the
``communication latency and model accuracy" trade-off, i.e., reduce the latency
by approximately 40-60% from the baseline, with slight accuracy degradation.
