In recent years, deep neural network has been widely used in many domains and got huge success. Since the size and computation workload for neural network model is increasing rapidly, GPU and many new-designed domain-specific accelerators have been used in order to complete computing neural networks as soon as possible. However, the traditional general-purpose processor should not be ignored. Considering it is common and easy to get, exploring efficient way for using general-purpose processor in deep learning is meaningful. In training phase, the multi-core architecture is suitable for data parallelism which helps to increase system throughput. However, in inference phase, end-to-end latency is much more important than throughput, and traditional data parallelism could not fulfill the requirement of small batch and low latency. In order to utilize hardware resource of multi-core architecture, it is necessary to split the computation task into smaller parts which can be executed on multi-core processor in parallel. Besides, a sophisticated strategy is necessary to make sure the split plan will not affect computing efficiency on each core. In this paper, we propose a parallel framework for the multi-core general-purpose processor. It divides each operation in the neural network into smaller ones and executes them on the multiple cores in parallel. By offering some necessary assistant operations, this framework can be easily transplanted to support potential multi-core processors. Also, the framework can automatically generate an effective splitting plan for the given neural networks. The plan is designed with enough consideration of both network architecture and low-level hardware. The experimental results show that this framework can give an efficient splitting plan which substantially reduces the end-to-end latency of inference task on multi-core processor.