The Future of Computing: An Energy-Efficient In-Memory Computing Architectures with Emerging VGSOT MRAM Technology

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Virginia Tech


This thesis work presents an unique architecture with a capacity of 1.57-Mb storage including in-memory compuitng capability, leveraging state-of-the-art gate voltage assisted spin-orbit torque (VGSOT) magnetic random-access memory (MRAM) technology. Beyond its role as a non-volatile storage solution, this architecture facilitates a diverse array of In-Memory Computing (IMC) operations, inclusive of logic-inside-memory (LinM/LiM), in-memory-dot- product multiplication tailored for binary-neural-networks, and content-accessable memory (CAM). Our designed bit-cell proposed in this architecture occupies a compact area of 0.195 μm2 and exhibits remarkable performance metrics. It achieves impressive writing speeds of 200 MHz and reading speeds of 1.5 GHz, applicable to non-volatile storage tasks and LinM operations. Notably, the LinM functionality supports a wide range of logical operations such as AND, NAND, OR, NOR, and MAJ, while the CAM feature enables efficient data searches of up to 1024 bits. Furthermore, in performance evaluations conducted using the MNIST and FMNIST datasets with a BNN model structured as 512-512-10 (input layer - hidden layer - output layer), the proposed VGSOT MRAM demonstrates exceptional inference accuracy. Specifically, it achieves a high accuracy rate of 97.40% for the MNIST dataset and 84.15% for the FMNIST dataset. In comparison to the 2T1R SOT-MRAM technology, the proposed VGSOT MRAM showcases significant advancements in read performance and reliability metrics. Notably, it features a 65.74% reduction in bit-cell area, alongside 84.78% and 33.4% lower read-write power consumption and 54.11% and 30.57% reduced LinM power consumption, respectively.



In-Memory Computing, MRAM, VGSOT, BNN