In order to master the current development status and development trends of memristor based neuromorphic chips, the existing memristor based neuromorphic chips and architectures were investigated. The memristor array structure and integration process, anterior and posterior neuron circuits, multi-array interconnection topology and data transmission strategy used in the chip, as well as the system simulation and evaluation methods used in the chip design process were compared and analyzed. It is concluded that the current circuit design of memristor based neuromorphic chips still need to solve the problems of limited resistance states, large device parameter fluctuation, complex array peripheral circuits, small integration scale, etc. It is pointed out that the actual application of this type of chip still faces challenges such as the improvement of memristor production process, improvement of development tool support, special instruction set development, and determination of typical traction applications.

To address the problem of how to faithfully map neural networks to resource-constrained embedded devices, a mixed-precision quantization method for convolutional neural networks based on layer sensitivity analysis was proposed. The sensitivity of convolutional layer parameters was measured by calculating the average trace of the Hessian matrix, providing a basis for bit-width allocation. A layer-wise ascending-descending approach was employed for bit-width allocation, ultimately achieving mixed-precision quantization of the network model. Experimental results demonstrate that compared to the fixed-precision quantization methods DoReFa and LSQ +, the proposed mixed-precision quantization method improves recognition accuracy by 10.2% and 1.7%, respectively, at an average bit-width of 3 bit. When compared to other mixed-precision quantization methods, the proposed approach achieves over 1% higher recognition accuracy. Additionally, noise-injected training effectively enhances the robustness of the mixed-precision quantization method, improving recognition accuracy by 16% under a noise standard deviation of 0.5.