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. / Master of Science / This work brings forth towards a new technology called VGSOT MRAM, which is a type of memory device that can store information without using extensive power. Its part of a larger architecture called IMC, which has many useful features. One of the main advantages of this technology is that it can perform different operations while storing data. For example, it can do calculations, search for specific information, and perform tasks for artificial intelligence networks. The design of the memory cells is also very efficient, taking up a small amount of space. In terms of performance, this technology is quite impressive. It can write data very quickly, at a speed of 250 million times per second, and read data even faster, at 1.67 billion times per second. It can also perform different logical operations, like AND, OR, and NAND, which are important for many computing tasks when tested with real-world tasks, such as recognizing images, this technology showed excellent accuracy. It achieved a recognition accuracy of 97.40% for the MNIST dataset and 84.15% for the FMNIST dataset, which is quite good. Compared to other similar technologies, this VGSOT MRAM has some advantages. It takes up less space, uses less power when reading and writing data, and consumes less power when performing calculations. These improvements make it a promising option for future devices.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119327 |
| Date | 19 April 2024 |
| Creators | Sarkar, Md Rubel |
| Contributors | Electrical and Computer Engineering, Walling, Jeffrey S., Yi, Cindy Yang, Jones, Creed F. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | ETD, application/pdf, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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