Compact optical convolution processing unit based on multimode interference

Meng, Xiangyan; Shi, Nuannuan; Li, Guangyi; Azaña, José; Capmany, J.; Yao, Jianping; Shen, Yichen; Li, Wei; Zhu, Na; Li, Ming

doi:10.1038/s41467-023-38786-x

Cited by 35 publications

(13 citation statements)

References 55 publications

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“…Hence, emerging photonic neural network (PNN) accelerators harness photonic devices to construct high-performance artificial intelligence processors that excel in terms of clock rate, throughput, and power efficiency 24 – 28 . The PNN accelerators process signals in the analog domain, and a variety of neural network models, including fully connected 29 – 33 , convolutional 34 – 37 , and recurrent 38 , have been successfully demonstrated. For machine vision tasks, PNN accelerators are used to process optical images directly in the analog domain 39 – 41 .…”

Section: Introductionmentioning

confidence: 99%

Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Xu,

Liu,

et al. 2024

Light Sci Appl

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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. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.

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Section: Introductionmentioning

confidence: 99%

Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Xu,

Liu,

et al. 2024

Light Sci Appl

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“…With the explosive growth of information in the era of artificial intelligence, the demand for the high efficiency of computing systems is extremely growing. Optical neural networks (ONNs) can achieve efficient computing with more accurate information extraction and fewer network parameters and are considered as a promising candidate for the next generation of neural morphology hardware processors. , That is because ONNs based on photonic integrated circuits (PICs) have unparalleled advantages in complex neuromorphic computing due to the inherent high parallelism, low interconnect loss, and ultrahigh computational bandwidth of up to 10 THz of photonics devices. − To date, many ONNs have been demonstrated for artificial intelligence, including pattern recognition, image classification, , and perceptron . However, in traditional on-chip ONNs, synaptic weights are determined by basic units of PICs by altering their optical phase or intensity.…”

Section: Introductionmentioning

confidence: 99%

Seven Bit Nonvolatile Electrically Programmable Photonics Based on Phase-Change Materials for Image Recognition

Xia,

Wang,

Wang

et al. 2024

ACS Photonics

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With the rapid development of the Internet of Things, how to efficiently store, transmit, and process massive amounts of data has become a major challenge now. Optical neural networks based on nonvolatile phase change materials (PCMs) have become a breakthrough point due to their zero static power consumption, low thermal crosstalk, large-scale, and high efficiency. However, current photonic devices cannot meet the multilevel requirements in neuromorphic computing due to their limited storage capacity. Here, a new way for increasing storage capacity is paved from the perspective of modulation of the crystallization kinetics of PCMs. A more progressive transition from the amorphous to the crystalline states is achieved through the grain-refinement phenomenon induced by nitrogen (N) element doping in Ge 2 Sb 2 Te 5 (GST), giving precise access to more multibit states. By integrating N-doped Ge 2 Sb 2 Te 5 (N-GST) with a waveguide, a high-capacity nonvolatile photonic device enabling over 7 bits (∼222 levels) storage is achieved for the first time. The storage capacity is increased nearly by 7 times compared to the state-of-the-art device (∼32 levels). An optical convolutional neural network is successfully established for the MINIST handwritten digit recognition task by mapping synapse weight to the multiple optical levels, and a recognition accuracy of up to 96.5% is achieved. Our work provides a new strategy for the development of integrated photonic devices with multilevel and demonstrates enormous application potential in the field of large-scale photonic neural networks.

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“…Also, the ONNs can get rid of the troubles caused by the Von Neumann bottleneck, avoiding the restrictions rooted in the energy and time consumption when reading and transmitting data from the memories 8 . With these benefits, the ONNs are proven to perform the image processing 9 – 16 , hand-written digits recognition 17 – 21 , and many other tasks 22 – 26 . The related techniques are also found to be applicable for logic 27 or matrix 28 – 30 operations.…”

Section: Introductionmentioning

confidence: 99%

Correlated optical convolutional neural network with “quantum speedup”

Sun,

Li,

Kong

et al. 2024

Light Sci Appl

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Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs have been implemented. However, the current ONNs cannot realize the acceleration as powerful as that indicated by the models like quantum neural networks. How to construct and realize an ONN with the quantum speedup is a huge challenge. Here, we propose theoretically and demonstrate experimentally a new type of optical convolutional neural network by introducing the optical correlation. It is called the correlated optical convolutional neural network (COCNN). We show that the COCNN can exhibit “quantum speedup” in the training process. The character is verified from the two aspects. One is the direct illustration of the faster convergence by comparing the loss function curves of the COCNN with that of the traditional convolutional neural network (CNN). Such a result is compatible with the training performance of the recently proposed quantum convolutional neural network (QCNN). The other is the demonstration of the COCNN’s capability to perform the QCNN phase recognition circuit, validating the connection between the COCNN and the QCNN. Furthermore, we take the COCNN analog to the 3-qubit QCNN phase recognition circuit as an example and perform an experiment to show the soundness and the feasibility of it. The results perfectly match the theoretical calculations. Our proposal opens up a new avenue for realizing the ONNs with the quantum speedup, which will benefit the information processing in the era of big data.

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Compact optical convolution processing unit based on multimode interference

Cited by 35 publications

References 55 publications

Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Seven Bit Nonvolatile Electrically Programmable Photonics Based on Phase-Change Materials for Image Recognition

Correlated optical convolutional neural network with “quantum speedup”

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