MS-SCIM: A Mixed-Signal Stochastic Computing-in-Memory Paradigm for Information Security

Stochastic Computing (SC), an emerging paradigm with advantages in hardware cost and fault tolerance, is well-suited for applications in image processing and information security. However, the existing SC paradigms suffer from significant hardware costs due to the conversion between binary and stochastic sequences, and the long latency caused by low computational parallelism. In this work, we demonstrate a Mixed-Signal Stochastic Computing-In-Memory (MS-SCIM) paradigm utilizing spin orbit torque magnetic random access memory (SOT-MRAM) arrays, in order to realize a energy-efficient and conversion-less SC method for the first time. The main contributions include: 1) The inherent stochastic switching behaviors of spintronic devices are exploited to enable the SOT-MRAM array to serve both as a parallel true random number generator (TRNG) and a CIM cell. 2) A high-parallelism mixed signal stochastic CIM paradigm is proposed to accelerate SC-based edge detection algorithm. The whole process achieves binary outputs without the conversion circuits and the energy efficiency achieves 446 Tops/W. 3) Based on the results of MS-SCIM, a novel image steganography method using stochastic bit streams is leveraged for information security, which enables lossless embedding and extraction of secret image information in a $156\times 156$ size, enhancing both capacity and undetectability.