Exploring Spin-transfer-torque devices and memristors for logic and memory applications
Abstract
As scaling CMOS devices is approaching its physical limits, researchers have begun exploring newer devices and architectures to replace CMOS. Due to their non-volatility and high density, Spin Transfer Torque (STT) devices are among the most prominent candidates for logic and memory applications. In this research, we first considered a new logic style called All Spin Logic (ASL). Despite its advantages, ASL consumes a large amount of static power; thus, several optimizations can be performed to address this issue. We developed a systematic methodology to perform the optimizations to ensure stable operation of ASL. Second, we investigated reliable design of STT-MRAM bit-cells and addressed the conflicting read and write requirements, which results in overdesign of the bit-cells. Further, a Device/Circuit/Architecture co-design framework was developed to optimize the STT-MRAM devices by exploring the design space through jointly considering yield enhancement techniques at different levels of abstraction. Recent advancements in the development of memristive devices have opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled researchers to solve a maze in hardware. In this research, we utilized swarm intelligence of memristive networks to perform image edge detection. First, we proposed a hardware-friendly algorithm for image edge detection based on ant colony. Next, we designed the image edge detection algorithm using memristive networks.
Degree
Ph.D.
Advisors
Roy, Purdue University.
Subject Area
Computer Engineering|Electrical engineering|Physics
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