Publications by Jih-Liang Hsieh

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Multimode fibers (MMF) were initially developed to transmit digital information encoded in the time domain. There were few attempts in the late 60s and 70s to transmit analog images through MMF. With the availability of digital spatial modulators, practica ...

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 ...

The escalating demands of artificial intelligence have underscored the limitations of traditional electronic computing, particularly in terms of power consumption and scalability. Optical computing offers a promising alternative, providing advantages in sp ...

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 ...

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 ...

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. ...

Jih-Liang Hsieh

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Learning to image and compute with multimode optical fibers

Programming nonlinear propagation for efficient optical learning machines

Programming the scalable optical learning operator with spatial-spectral optimization

Programming Nonlinear Computation with Nonlinear and Linear Optics

Resource-efficient photonic networks for next-generation AI computing

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Modelling and Integrating Nonlinear Optical Transformations in Neural Networks