Optimization of Magnetic Tunneling Junction Devices for Neuromorphic Circuits for Solving MAXCUT

Ian Mulet, Bradley H. Theilman, Karan P. Patel, Jared Arzate, Andrew Maicke, J. Darby Smith, James B. Aimone, Suma Cardwell, Jean Anne C. Incorvia, Catherine D. Schuman

Abstract: Novel algorithms leveraging neuromorphic computation are on the forefront of algorithm design. Here, we investigate how stochastic devices integrate and perform with a novel neuromorphic algorithm for solving MAXCUT problems in graphs. We evaluate how using magnetic tunneling junctions (MTJs) as the device to generate random numbers impacts the neuromorphic MAXCUT algorithm. We use both experimental MTJ data, as well as a model of the device behavior to investigate MTJ performance on this task. We also leverage the use of evolutionary optimization to tune the MTJ device to maximize performance on the algorithm and minimize energy usage of the device.