A High-Accuracy MRAM-Based Computing-in-Memory Macro for Secure Edge AI Inference

Computing-in-memory (CIM) represents a pivotal technology for overcoming the speed and power bottlenecks posed by the “memory wall” and “power wall” existing in von Neumann architectures. With the fast development of Internet of Things, data security has become one of the most attractive research topics for CIM in edge applications, as well as computing accuracy and energy efficiency. This paper proposes a highly accurate and secure MRAM-based CIM macro that is designed to reduce the multiply accumulate (MAC) computation errors and protect weight bits in untrusted environments. The architecture employs a series-connected structure to enhance computational linearity and introduces a dynamic reference column to increase reliability. Meanwhile, a lightweight encryption mechanism based on physical unclonable function (PUF) is implemented to protect weight bits. The results demonstrate that, after the obfuscation, the prediction accuracy of machine learning-based attacks on the PUF is reduced to approximately 50%. The CIM macro achieves impressive inference accuracy of 93.73% on the CIFAR-10 dataset with energy efficiency of 38.3 TOPS/W. Furthermore, security verification performed on the CNN model indicates that weight encryption degrades inference accuracy to 10%, thereby providing robust protection against potential attacks.