DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.001
Publication date: 9th January 2023
Neuromorphic computing is seeing a resurgence as Si scaling strategies offer diminished returns. Software leads the way in implementing intelligence noted in animal brains into artificial neural network algorithms and learning rules. Simultaneously, there is interest in examining how such features can be emulated in hardware and whether this can offer some practical advantages in realizing low power computers in general or computers that can perform certain tasks more efficiently. The focus of my presentation will be on metal-insulator transitions in quantum materials such as correlated perovskite semiconductors that can be used to emulate the characteristics of neurons and synapses in the brain and serve as building blocks for AI hardware. We will then discuss how features of intelligence noted in various insects, mammals and birds by biologists can be implemented in electronic devices and circuits for learning and decision-making. Fundamental microscopic mechanisms essential to understanding the operational principles of such devices will be discussed using combination of synchrotron scattering studies and first principles modeling. Connections to the early research on spin glasses for neural networks will be discussed. Collaborations and funding will be acknowledged in the presentation.