Abstract: Memristor is one of the most promising candidates to build processing-in-memory (PIM) structures. The memristor-based PIM with digital or multi-level memristors has been proposed for neuromorphic computing. The essential frequent AD/DA converting and intermediate memory in these structures leads to significant energy and area overhead. To address this issue, a memristor-based PIM architecture for deep convolutional neural network (CNN) is proposed in this work. It exploits the analog architecture to eliminate data converting in neuron layer banks, each of which consists of two special modules named weight sub-arrays (WSAs) and accumulate sub-arrays (ASAs). The partial sums of neuron inputs are generated in WSAs concurrently and are written into ASAs continuously, in which the results are computed finally. The noise in proposed analog style architecture is analyzed quantitatively in both model and circuit levels, and a synthetic solution is presented to suppress the noise, which calibrates the non-linear distortion of weight with a corrective function, pre-charges the write module to reduce the parasitic effects, and eliminates noise with a modified noise-aware training. The proposed design has been evaluated by varying neural network benchmarks, in which the results show that the energy efficiency and performance can both be improved about 90% in specific neural network without accuracy losses compared with digital solutions.