Prospects and applications of photonic neural networks
C Huang, VJ Sorger, M Miscuglio… - … in Physics: X, 2022 - Taylor & Francis
Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …
learning, and neuromorphic computing. Software implementations of neural networks on …
Quantumnas: Noise-adaptive search for robust quantum circuits
Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ)
computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …
computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …
Light in ai: toward efficient neurocomputing with optical neural networks—a tutorial
In the post Moore's era, conventional electronic digital computing platforms have
encountered escalating challenges to support massively parallel and energy-hungry …
encountered escalating challenges to support massively parallel and energy-hungry …
On the convergence of zeroth-order federated tuning for large language models
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering
in a new era in privacy-preserving natural language processing. However, the intensive …
in a new era in privacy-preserving natural language processing. However, the intensive …
A compact butterfly-style silicon photonic–electronic neural chip for hardware-efficient deep learning
The optical neural network (ONN) is a promising hardware platform for next-generation
neurocomputing due to its high parallelism, low latency, and low energy consumption …
neurocomputing due to its high parallelism, low latency, and low energy consumption …
Control-free and efficient integrated photonic neural networks via hardware-aware training<? TeX\break?> and pruning
Integrated photonic neural networks (PNNs) are at the forefront of AI computing, leveraging
light's unique properties, such as large bandwidth, low latency, and potentially low power …
light's unique properties, such as large bandwidth, low latency, and potentially low power …
Deepzero: Scaling up zeroth-order optimization for deep model training
Zeroth-order (ZO) optimization has become a popular technique for solving machine
learning (ML) problems when first-order (FO) information is difficult or impossible to obtain …
learning (ML) problems when first-order (FO) information is difficult or impossible to obtain …
L2ight: Enabling on-chip learning for optical neural networks via efficient in-situ subspace optimization
Silicon-photonics-based optical neural network (ONN) is a promising hardware platform that
could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility, ultra …
could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility, ultra …
Tensor-compressed back-propagation-free training for (physics-informed) neural networks
Backward propagation (BP) is widely used to compute the gradients in neural network
training. However, it is hard to implement BP on edge devices due to the lack of hardware …
training. However, it is hard to implement BP on edge devices due to the lack of hardware …
Free-space optical multiplexed orbital angular momentum beam identification system using Fourier optical convolutional layer based on 4f system
Here we introduce a free-space optical communication (FSOC) system that is capable of
adjustment to alignment drift, varying atmospheric turbulent conditions of multiplexed spatial …
adjustment to alignment drift, varying atmospheric turbulent conditions of multiplexed spatial …