In-Memory Computing Architecture for Efficient Hardware Security

This paper presents an innovative approach utilizing in-memory computing (IMC) for the development and integration of AES (Advanced Encryption Standard) cipher technique. Our research aims to enhance cybersecurity measures for a wide range of applications for IoT, such as robotic self-driving and several uses contexts. Memristor (MR) design optimized for in-memory processing is introduced. Our work highlights the development of a 4-bit state memristor device tailored for various range of arithmetic functions in a hardware prototype of AES system. Additionally, we propose a pipeline AES design aimed at harnessing extensive parallelism and ensuring compatibility with MR devices. This approach enhances hardware performance by by managing larger data amounts, accelerating computational, and achieving greater precision demands. Compared to traditional AES hardware, AES-IMC demonstrates an approximate $30 \%$ improvement in power with a comparable throughput rate. Compared with the latest AES-based NVM engines, AES-IMC achieves an impressive $62 \%$ improvement in throughput at similar power dissipation levels. The IMC-developed design will protect against unintentional incidents involving unmanned devices, reducing the risks associated with hostile assaults such as hijacking and illegal control of robots. This helps to reduce the possible economic and financial losses caused by incidents.