Compact optical convolution processing unit based on multimode interference
Convolutional neural networks are an important category of deep learning, currently facing
the limitations of electrical frequency and memory access time in massive data processing …
the limitations of electrical frequency and memory access time in massive data processing …
Space-efficient optical computing with an integrated chip diffractive neural network
Large-scale, highly integrated and low-power-consuming hardware is becoming
progressively more important for realizing optical neural networks (ONNs) capable of …
progressively more important for realizing optical neural networks (ONNs) capable of …
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 …
Chip-based high-dimensional optical neural network
X Wang, P Xie, B Chen, X Zhang - Nano-Micro Letters, 2022 - Springer
Parallel multi-thread processing in advanced intelligent processors is the core to realize
high-speed and high-capacity signal processing systems. Optical neural network (ONN) has …
high-speed and high-capacity signal processing systems. Optical neural network (ONN) has …
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 …
Single chip photonic deep neural network with accelerated training
As deep neural networks (DNNs) revolutionize machine learning, energy consumption and
throughput are emerging as fundamental limitations of CMOS electronics. This has …
throughput are emerging as fundamental limitations of CMOS electronics. This has …
A multichannel optical computing architecture for advanced machine vision
Endowed with the superior computing speed and energy efficiency, optical neural networks
(ONNs) have attracted ever-growing attention in recent years. Existing optical computing …
(ONNs) have attracted ever-growing attention in recent years. Existing optical computing …
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 …
Inverse design of an integrated-nanophotonics optical neural network
Artificial neural networks have dramatically improved the performance of many machine-
learning applications such as image recognition and natural language processing …
learning applications such as image recognition and natural language processing …
Single-shot optical neural network
Analog optical and electronic hardware has emerged as a promising alternative to digital
electronics to improve the efficiency of deep neural networks (DNNs). However, previous …
electronics to improve the efficiency of deep neural networks (DNNs). However, previous …