We formulate a multi-objective optimization problem and propose the LMOS algorithm to achieve a Pareto efficient solution. In this paper, we assert that running CNNs between an edge device and the cloud is synonymous to solving a resource-constrained optimization problem that minimizes the latency and maximizes resource utilization at the edge. Splitting the CNN architecture to perform part of the computation on edge and remaining on the cloud is an area of research that has seen increasing interest in the field. Many such computations utilize Convolution Neural Networks (CNNs) to perform AI tasks, having high resource and computation requirements, that are infeasible for edge devices. With the increasing reliance of users on smart devices, bringing essential computation at the edge has become a crucial requirement for any type of business. The proposed approach, SmartSplit fares better when compared to other state-of-the-art approaches. Our experiments run with multiple CNN models show that splitting a CNN between a smartphone and a cloud server is feasible. We design SmartSplit, a Genetic Algorithm with decision analysis based approach to solve the optimisation problem. 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. ![]() 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. 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. ![]() Convolution Neural Networks (CNNs), which are used by several AI applications, are highly resource and computation intensive. 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.
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