  Artificial Intelligence has now taken centre stage in the smartphone industry
owing to the need of bringing all processing close to the user and addressing
privacy concerns. Convolution Neural Networks (CNNs), which are used by several
AI applications, are highly resource and computation intensive. Although new
generation smartphones come with AI-enabled chips, minimal memory and energy
utilisation is essential as many applications are run concurrently on a
smartphone. In light of this, optimising the workload on the smartphone by
offloading a part of the processing to a cloud server is an important direction
of research. In this paper, we analyse the feasibility of splitting CNNs
between smartphones and cloud server by formulating a multi-objective
optimisation problem that optimises the end-to-end latency, memory utilisation,
and energy consumption. We design SmartSplit, a Genetic Algorithm with decision
analysis based approach to solve the optimisation problem. Our experiments run
with multiple CNN models show that splitting a CNN between a smartphone and a
cloud server is feasible. The proposed approach, SmartSplit fares better when
compared to other state-of-the-art approaches.
