The invention provides an in-memory pulse neural network based on current integration. Calculation based on a charge domain is naturally compatible with a working mechanism of neurons. In one aspect, in order to avoid non-ideality of employing NVM materials, memory cells of a synaptic array in an architecture employ silicon-based SRAM cells. In addition, the modified NVM unit can benefit from the architecture of the built-in pulse neural network designed by the invention. When the synaptic array adopts an SRAM (Static Random Access Memory) unit as a storage unit, the design of a post-neuron circuit corresponds to the SRAM unit, so that the SNN architecture in the storage can be used for calculating a multi-bit synaptic weight, and the combined column number is programmable. Further, in order to improve the use efficiency of the area and save energy efficiency, in the calculation of the multi-bit synaptic weight, the circuit is designed to be in a time multiplexing form of resource sharing. Finally, an automatic calibration circuit is provided to counteract the change of the conduction current caused by factors such as process, voltage, temperature (PVT) and the like, so that the calculation result is more accurate.