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Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved…

Computer Vision and Pattern Recognition · Computer Science 2023-11-29 Gourav Datta , Zeyu Liu , Anni Li , Peter A. Beerel

Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration. In this paper, we propose a…

Networking and Internet Architecture · Computer Science 2023-06-13 Boning Li , Timofey Efimov , Abhishek Kumar , Jose Cortes , Gunjan Verma , Ananthram Swami , Santiago Segarra

Physics-informed neural networks (PINNs) solve time-dependent partial differential equations (PDEs) by learning a mesh-free, differentiable solution that can be evaluated anywhere in space and time. However, standard space--time PINNs take…

Machine Learning · Computer Science 2026-01-29 Chen-Yang Dai , Che-Chia Chang , Te-Sheng Lin , Ming-Chih Lai , Chieh-Hsin Lai

Tensor networks (TNs) enable compact representations of large tensors through shared parameters. Their use in probabilistic modeling is particularly appealing, as probabilistic tensor networks (PTNs) allow for tractable computation of…

Machine Learning · Computer Science 2025-10-02 Marawan Gamal Abdel Hameed , Guillaume Rabusseau

Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal…

Optics · Physics 2025-01-03 Reyhane Ahmadi , Amirreza Ahmadnejad , Somayyeh Koohi

With the continuous increase in the size and complexity of machine learning models, the need for specialized hardware to efficiently run such models is rapidly growing. To address such a need, silicon-photonic-based neural network (SP-NN)…

Emerging Technologies · Computer Science 2023-08-08 Amin Shafiee , Sanmitra Banerjee , Krishnendu Chakrabarty , Sudeep Pasricha , Mahdi Nikdast

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms, but also energy-efficient…

Neural and Evolutionary Computing · Computer Science 2020-01-16 Yusuke Sakemi , Kai Morino , Takashi Morie , Kazuyuki Aihara

Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn long-term correlations in the data. This approach…

Machine Learning · Computer Science 2017-04-04 Li Jing , Yichen Shen , Tena Dubček , John Peurifoy , Scott Skirlo , Yann LeCun , Max Tegmark , Marin Soljačić

Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large number of arithmetic operations and memory accesses.…

Machine Learning · Computer Science 2023-12-18 Xi Chen , Chang Gao , Zuowen Wang , Longbiao Cheng , Sheng Zhou , Shih-Chii Liu , Tobi Delbruck

Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN…

Optics · Physics 2024-11-21 Sahar Behroozinia , Qing Gu

Fully Connected Neural Network (FCNN) is a class of Artificial Neural Networks widely used in computer science and engineering, whereas the training process can take a long time with large datasets in existing many-core systems. Optical…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-11-29 Fei Dai , Yawen Chen , Haibo Zhang , Zhiyi Huang

Binary neural networks (BNNs) have demonstrated their ability to solve complex tasks with comparable accuracy as full-precision deep neural networks (DNNs), while also reducing computational power and storage requirements and increasing the…

Machine Learning · Computer Science 2022-07-12 Riccardo Schiavone , Maria A. Zuluaga

Parametrized quantum circuits are essential components of variational quantum algorithms. Until now, optical implementations of these circuits have relied solely on adjustable linear optical units. In this study, we demonstrate that using…

Quantum Physics · Physics 2025-01-22 E. A. Chernykh , M. Yu. Saygin , G. I. Struchalin , S. P. Kulik , S. S. Straupe

The rapid growth in computing demands, particularly driven by artificial intelligence applications, has begun to exceed the capabilities of traditional electronic hardware. Optical computing offers a promising alternative due to its…

Hardware Architecture · Computer Science 2025-07-24 Shupeng Ning , Hanqing Zhu , Chenghao Feng , Jiaqi Gu , David Z. Pan , Ray T. Chen

In recent years, Orthogonal Recurrent Neural Networks (ORNNs) have gained popularity due to their ability to manage tasks involving long-term dependencies, such as the copy-task, and their linear complexity. However, existing ORNNs utilize…

Neural and Evolutionary Computing · Computer Science 2024-06-11 Armand Foucault , Franck Mamalet , François Malgouyres

Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness. The most effective way to train deep SNNs is through…

Neural and Evolutionary Computing · Computer Science 2022-02-04 Tong Bu , Jianhao Ding , Zhaofei Yu , Tiejun Huang

A long-standing proposition is that by emulating the operation of the brain's neocortex, a spiking neural network (SNN) can achieve similar desirable features: flexible learning, speed, and efficiency. Temporal neural networks (TNNs) are…

Neural and Evolutionary Computing · Computer Science 2021-02-24 James E. Smith

Edholm's Law predicts exponential growth in data rate and spectrum bandwidth for communications and is forecasted to remain true for the upcoming deployment of 6G. Compounding this issue is the exponentially increasing demand for deep…

Emerging Technologies · Computer Science 2024-06-10 Ronald Davis , Zaijun Chen , Ryan Hamerly , Dirk Englund

Timing synchronization (TS) is one of the key tasks in orthogonal frequency division multiplexing (OFDM) systems. However, multi-path uncertainty corrupts the TS correctness, making OFDM systems suffer from a severe…

Signal Processing · Electrical Eng. & Systems 2023-07-04 Chaojin Qing , Na Yang , Shuhai Tang , Chuangui Rao , Jiafan Wang , Hui Lin

Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-03-14 Jiantong Jiang , Zeyi Wen , Atif Mansoor , Ajmal Mian
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