Nowadays, a wide range of intelligent applications rely on deep neural networks (DNNs), ranging from face recognition to autonomous driving. Inference on pre-trained DNN is accurate and efficient, but resource-intensive, especially for end devices such as smartphone and wearable devices. To address the associated resource constraints, DNN inference tasks are often offloaded to the edge or cloud, achieved by partitioning the DNN and offloading part of the computation to another device. However, most of the existing solutions usually adopt more complex methods such as reinforcement learning and heuristic algorithms. In contrast, this paper introduces a simple approach by modeling the end-edge collaborative DNN inference system via the Environments - Classes, Agents, Roles, Groups, Objects (E-CARGO) model, and Role-Based Collaboration (RBC) methodology. A DNN partition point selection algorithm is proposed and the DNN task assignment problem in the system is formulated as a Group Multi-Role Assignment(GMRA) problem to be solved. Extensive simulation experiments demonstrate that the proposed solution can effectively reduce the global delay of DNN inference.