Publications Details

Device Codesign using Reinforcement Learning

Cardwell, Suma G.; Patel, Karan; Schuman, Catherine D.; Smith, J.D.; Kwon, Jaesuk; Maicke, Andrew; Arzate, Jared; Incorvia, Jean A.C.

We demonstrate device codesign using reinforcement learning for probabilistic computing applications. We use a spin orbit torque magnetic tunnel junction model (SOT-MTJ) as the device exemplar. We leverage reinforcement learning (RL) to vary key device and material properties of the SOT-MTJ device for stochastic operation. Our RL method generated different candidate devices capable of generating stochastic samples for a given exponential distribution.