## 1 Introduction  
Internet of things (IoT), edge computing (EC), and artificial intelligence (AI) are three technological pillars of the current industrial revolution [1; 2]. One hot topic in applying these techniques is edge intelligence [3; 4; 5], which involves running deep learning algorithms at the edge of the networks instead of entirely offloading the inferencing tasks to the cloud center. As edge intelligence can alleviate problems such as large bandwidth occupancy, high transmission delay, slow response speed, poor network reliability, and leakage of personal or sensitive information, it is being intensively researched and widely used today. For example, it is common to deploy a trained convolutional neural network (CNN) to the edge for performing real-time video analysis in applications including autonomous driving [6], intelligent monitoring [7], industrial IoT [8], smart cities [9], etc. However, as CNN inference is usually computationally intensive and some CNNs are huge, it is often infeasible to deploy a complete CNN model or perform inference on a single-edge device. On one hand, the above problem can be solved by pruning, quantization, or knowledge distillation [10; 11; 12] to obtain a smaller DNN model before deploying and inferencing. However, these techniques may sacrifice model inference accuracy somewhat. On the other hand, the powerful cloud server is usually adopted to perform part of the inferencing task [13; 14; 15]. In such a cloud-assisted approach, a DNN model is often divided into two parts: one remains locally and the other runs at the remote cloud server. As with offloading a complete DNN model to the cloud server, cloud-assisted approaches also need to face the problem of private data leakage, which is natural in cloud computing. Additionally, balancing the calculation accuracy, end-to-end delay, and resource occupancy is also challenging. To fully unleash the potential of edge devices, it is popular to cut a neural network into multiple pieces and distribute them among available edge devices to perform the inference cooperatively [16; 17]. This approach could overcome the problems above by keeping the inferencing process in the edge network. Nevertheless, it is more challenging to partition and distribute a neural network to achieve optimal performance, as it is an NP-hard problem. Although some strategies have been developed in an attempt to split a DNN into several parts effectively [18; 19; 20], most of them pay more attention to the methodology of reorganizing the network structure rather than optimizing the process for getting an optimal solution from the perspective of the actual system running. Hence, the problem of partitioning a DNN model to achieve optimal deployment has not been adequately addressed. This paper proposes a novel layer-based partitioning approach to obtain an optimal DNN deployment solution. In order to ensure the applicability of the resulting deployment scheme, the partitioning problem is defined as a constrained optimization problem and an improved genetic algorithm (GA) is proposed to ensure the generation of feasible candidate solutions after each crossover and mutation operation. Compared to the basic GA, the proposed GA in this paper results a running time that is one to three times shorter than that of the basic GA, while obtaining a better deployment. The main contributions of this paper are as follows:  
* Firstly, the DNN model partitioning problem is modeled as a constrained optimization problem and the corresponding problem is introduced. * Secondly, the paper puts forward a novel genetic algorithm to shorten solving time by ensuring the validity of chromosomes after crossover and mutation operation. * Finally, experiments are performed on several existing DNN models, including AlexNet, ResNet110, MobelNet, and SqueezeNet, to present a more comprehensive evaluation. The remainder of this paper is organized as follows: Section 2 gives an overview of the related work. Section 3 presents the problem definition of the DNN partition problem.