Publications de Mustafa Yildirim

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Computing has long been central to technological advancements, where each leap in compute power has catalyzed applications once thought impossible, such as smartphones. Recently, the exponential growth in the compute demand of artificial intelligence (AI) ...

The 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 com ...

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical tech ...

A device (1) and method for processing data comprises at least one input layer (2), at least one modulation layer (3), and at least one output layer (4). The input layer (2) is configured to receive at least one carrier signal (Sc). The modulation layer (3 ...

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement meth ...

Current trends in artificial intelligence toward larger models demand a rethinking of both hardware and algorithms. Photonics-based systems offer high-speed, energy-efficient computing units, provided algorithms are designed to exploit photonics' unique st ...

Optical neural networks are expected to be hybrid systems for the foreseeable future combining optics and electronics. The linear portion of the system, consisting of the weighted interconnections between the neurons, can be realized optically. The nonline ...

Modern machine learning models use an ever-increasing number of parameters to train (175 x 10(9) parameters for GPT-3) with large datasets to achieve better performance. Optical computing has been rediscovered as a potential solution for large-scale data p ...

A novel optical computing framework by harnessing spatiotemporal nonlinear effects of multimode fibers for machine learning is presented. With linear and nonlinear interactions of the spatial fiber modes, a brain-inspired computation engine is experimental ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

As microfiber-based additive manufacturing (AM) technologies, melt electrowriting (MEW) and solution electrowriting (SEW) have demonstrated efficacy with more biomedically relevant materials. By processing SU-8 resin using MEW and SEW techniques, a materia ...

We experimentally demonstrate a hybrid optical-digital neural network utilizing a high-dimensional nonlinear optics system. Precise digital modelling enables the system to achieve competitive performance in machine learning tasks. ...

Mustafa Yildirim

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Optical Processors for AI Computing and Networking

Programming nonlinear propagation for efficient optical learning machines

Programming the scalable optical learning operator with spatial-spectral optimization

Optical data processing using structural non-linearity

Learning to See and Compute through Multimode Fibers

Resource-efficient photonic networks for next-generation AI computing

On the implementation of nonlinearities in optical neural networks: opinion

Nonlinear optical feature generator for machine learning

Machine learning with multimode fibers

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Electrowriting of SU-8 Microfibers

Modelling and Integrating Nonlinear Optical Transformations in Neural Networks

Optical computing with spatiotemporal fiber nonlinearities