User profiles for Shaofu Xu
Shaofu XuShanghai Jiao Tong University Verified email at sjtu.edu.cn Cited by 1027 |
Microcomb-based integrated photonic processing unit
The emergence of parallel convolution-operation technology has substantially powered the
complexity and functionality of optical neural networks (ONN) by harnessing the dimension …
complexity and functionality of optical neural networks (ONN) by harnessing the dimension …
High-order tensor flow processing using integrated photonic circuits
Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal
processing. To increase computing throughput, mainstream processors transform tensor …
processing. To increase computing throughput, mainstream processors transform tensor …
Optical coherent dot-product chip for sophisticated deep learning regression
Optical implementations of neural networks (ONNs) herald the next-generation high-speed
and energy-efficient deep learning computing by harnessing the technical advantages of …
and energy-efficient deep learning computing by harnessing the technical advantages of …
Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics
Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive
sensing systems since key features are much sparser than the Nyquist-sampled data. …
sensing systems since key features are much sparser than the Nyquist-sampled data. …
Deep-learning-powered photonic analog-to-digital conversion
Analog-to-digital converters (ADCs) must be high speed, broadband, and accurate for the
development of modern information systems, such as radar, imaging, and communications …
development of modern information systems, such as radar, imaging, and communications …
High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays
Optical neural networks (ONNs) have become competitive candidates for the next generation
of high-performance neural network accelerators because of their low power consumption …
of high-performance neural network accelerators because of their low power consumption …
Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines
Recent progress on optical neural networks (ONNs) heralds a new future for efficient deep
learning accelerators, and novel, to the best of our knowledge, architectures of optical …
learning accelerators, and novel, to the best of our knowledge, architectures of optical …
Optical convolutional neural network with WDM-based optical patching and microring weighting banks
We propose an optical convolutional neural network (OCNN) architecture for high-speed
and energy-efficient deep learning accelerators. The WDM-based optical patching scheme (…
and energy-efficient deep learning accelerators. The WDM-based optical patching scheme (…
A review: Photonics devices, architectures, and algorithms for optical neural computing
The explosive growth of data and information has motivated various emerging non-von
Neumann computational approaches in the More-than-Moore era. Photonics neuromorphic …
Neumann computational approaches in the More-than-Moore era. Photonics neuromorphic …
Modified deep-learning-powered photonic analog-to-digital converter for wideband complicated signal receiving
We propose and demonstrate a modified deep-learning-powered photonic analog-to-digital
converter (DL-PADC) in which a neural network is used to eliminate the signal distortions of …
converter (DL-PADC) in which a neural network is used to eliminate the signal distortions of …