Evaluation of Stochastic Magnetic Tunnel Junctions as Building Blocks for Probabilistic Computing
Abstract
Probabilistic computing has been proposed as an attractive alternative for bridging the computational gap between the classical computers of today and the quantum computers of tomorrow. It offers to accelerate the solution to many combinatorial optimization and machine learning problems of interest today, motivating the development of dedicated hardware. Similar to the ‘bit’ of classical computing or ‘q-bit’ of quantum computing, probabilistic bit or ‘p-bit’ serve as a fundamental building-block for probabilistic hardware. p-bits are robust classical quantities, fluctuating rapidly between its two states, envisioned as three-terminal devices with a stochastic output controlled by its input. It is possible to implement fast and efficient hardware p-bits by modifying the present day magnetic random access memory (MRAM) technology. In this dissertation, we evaluate the design and performance of low-barrier magnet (LBM) based p-bit realizations. LBMs can be realized from perpendicular magnets designed to be close to the in-plane transition or from circular in-plane magnets. Magnetic tunnel junctions (MTJs) built using these LBMs as free layers can be integrated with standard transistors to implement the three-terminal p-bit units. A crucial parameter that determines the response of these devices is the correlation-time of magnetization. We show that for magnets with low energy barriers (Δ ≤ kBT) the circular disk magnets with in-plane magnetic anisotropy (IMA) can lead to correlation-times in sub-nstimescales; two orders of magnitude smaller compared to magnets having perpendicular magnetic anisotropy (PMA). We show that this striking difference is due to a novel precession-like fluctuation mechanism that is enabled by the large demagnetization field in mono-domain circular disk magnets. Our predictions on fast fluctuations in LBM magnets have recently received experimental confirmation as well. We provide a detailed energy-delay performance evaluation of the stochastic MTJ (s-MTJ) based p-bit hardware. We analyze the hardware using benchmarked SPICE multi-physics modules and classify the necessary and sufficient conditions for designing them. We connect our device performance analysis to systems-level metrics by emphasizing problem and substrate independent figures-of-merit such as flips per second and dissipated energy per flip that can be used to classify probabilistic hardware.
Degree
Ph.D.
Advisors
Datta, Purdue University.
Subject Area
Computer science|Statistics
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