Optoelectronic convolutional neural networks based on time-stretch method

Abstract

In this paper, a new architecture of optoelectronic convolutional neural networks (CNNs) based on time-stretch method is proposed. In this loop-shaped structure mainly composed of fiber optical and electronic devices, computations of data from each layer of CNN which are carried by light pulses with high repetition rate can be accomplished in a serial way. Therefore, a 5-layer CNN with two convolution layers, two mean pooling layers and one fully-connected layer are implemented. Under the test of handwriting digit recognition, its accuracy can reach up to 95% under ideal circumstances. Tests under different relative noise levels have been conducted and analyzed as well.

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

“…As seen from Equation ( 14), real-valued VMM can be performed by adjusting the coupling coefficient of each MRR. Additionally, WDM-based CNNs with serial data input approaches have also been reported [35,96,[101][102][103][105][106][107][108][109][110]. As shown in Figure 6d, the weight vector W was still weighted on the amplitudes of the wavelength components, and the 1D data X were serially modulated to the amplitude of the wavelength components.…”

“…Then, wavelength components underwent amplitude modulation to load the weight vector W, which was the multiplication process of elements in two vectors. As a main optical device in the WDM-based CNN, the variable optical amplitude modulators can be implemented using MZIs [95], microring resonators (MRRs) [96][97][98][99][100][101][102][103], phase change materials (PCMs) [39,104], and waveshapers [35,[105][106][107][108][109]. The addition process was conducted using square-law photoelectric detection and the summation of the optical intensity of wavelength components at the photodetector.…”

Dual Optical Frequency Comb Neuron: Co‐Developing Hardware and Algorithm

“…Researchers worldwide have contributed many representative works, further deepening the integration of photonics and artificial intelligence. Typical PNNs include fiber optic networks, [8][9][10] free space optic networks, [11,12] and integrated photonic circuits. [13][14][15] Each solution has its own characteristics, but they all share the same goal of exploiting the potential benefits of PNNs' large bandwidth and high energy efficiency [13,[16][17][18][19][20][21] to break bread with electronic digital processors represented by graphics processing units in the third artificial intelligence boom.…”

Classification of time-domain waveforms using a speckle-based optical reservoir computer

“…Electrical neural networks require the storage of large matrices of weight coefficients, which inherently slows down the computation as the information must be passed back and forth between the memory and processing units and is energy costly. In an optical neural network, the weight coefficients are an intrinsic part of the photonic system, and the information is transmitted at the speed of light [11].…”

“…Photonic reservoir computing (RC), in particular, has attracted a lot of attention [2,3], as it can be used for a wide variety of classification, prediction, and memory tasks [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Due to its recurrent nature, it can mimic and predict the complex nonlinear temporal dynamics of various systems with memory [22,23].…”