With the continuous development of deep learning and neural networks,the immense computational demand presented challenges for traditional von Neumann architecture devices.Consequently,Compute-In-Memory(CIM)become the prevailing design direction to meet the high timeliness requirements and compute-intensive demands of neural networks.A dedicated neural network accelerator is designed to provide high-speed solutions for high-density data.The ResNet14 neural network is quantified at first,and a digital system oriented towards Compute-In-Memory is designed based on net's structure.To enhance the system's adaptability to multiple networks,a compatibility concept is proposed,enable the digital system to accommodate partial convolutional layers of ResNet18 or other convolutional neural networks(CNN).Finally,the system is deployed on an FPGA for verification.Under a clock frequency of 10 MHz,target classification tasks are performed on the Cifar-10 and MNIST datasets,resulting in accuracy rates of 84.17%and 98.79%respectively at 60 FPS,that means this design has smaller data width and similar accuracy.
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