Researcher: Teğin, Uğur
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Teğin, Uğur
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Publication Open Access Implementing the analogous neural network using chaotic strange attractors(Springer Nature, 2024) Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Teğin, Uğur; College of EngineeringMachine learning studies need colossal power to process massive datasets and train neural networks to reach high accuracies, which have become gradually unsustainable. Limited by the von Neumann bottleneck, current computing architectures and methods fuel this high power consumption. Here, we present an analog computing method that harnesses chaotic nonlinear attractors to perform machine learning tasks with low power consumption. Inspired by neuromorphic computing, our model is a programmable, versatile, and generalized platform for machine learning tasks. Our mode provides exceptional performance in clustering by utilizing chaotic attractors’ nonlinear mapping and sensitivity to initial conditions. When deployed as a simple analog device, it only requires milliwatt-scale power levels while being on par with current machine learning techniques. We demonstrate low errors and high accuracies with our model for regression and classification-based learning tasks.Publication Metadata only Programming nonlinear propagation for efficient optical learning machines(SPIE, 2024) Oguz, Ilker; Hsieh, Jih-Liang; Dinc, Niyazi Ulas; Yildirim, Mustafa; Gigli, Carlo; Moser, Christophe; Psaltis, Demetri; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Teğin, Uğur; College of EngineeringThe ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation, since light propagation through a nonabsorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMFs) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. We propose an optical neural network architecture that performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.