LOEN: Lensless opto-electronic neural network empowered machine vision

Abstract

Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. Compared with the random MLS pattern, the recognition accuracy is improved by more than 6%.

Verification methods for complex-functional blocks in CAD for chips deep submicron design standards

“…Unlike very large-scale integrated circuits (VLSI) -digital or digital-analog -(in a generalized form, this process is shown in Fig. 2), SoC verification has certain features and sometimes is a rather complicated task [4][5][6]. These features include:…”

Integrated Photonic Convolutional Neural Network Based on Silicon Metalines

“…To address the energy consumption bottleneck of CMOS electronics in machine learning [9], researchers are investigating optical computing as a potential alternative architecture for building neural networks. Optical parallelism has been used in ONN [10], [11], [12], [13] and Opto-Electronic Neural network implementations [14], [15], [16], [17], [18] to speed up computing, while optical passivity has been utilized to reduce energy costs and minimize latency. Several studies have reported the successful implementation of ONN designs utilizing multi-layer metasurfaces [10], [11], [12], [19].…”

Metasurface Smart Glass for Object Recognition

“…An ONN utilizes photonic elements and circuits to form a layered architecture emulating that of digital ANNs to directly process optical signals from target objects [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. In an ideal ONN, optical signals are manipulated by layers of elements in sequence that perform linear transformations and nonlinear activations, which are pre-trained to enable the network to perform device-specific computing tasks.…”

“…ONNs have been demonstrated in a number of optical platforms (Table S1) [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

“…Furthermore, the utility of the neural networks would be hampered by their large dimensions and a lack of wide availability of SLMs and THz sources and detectors. More recently, hybrid optoelectronic neural networks that combine the strength of an optical frontend in linear transformation and that of a digital neural network in nonlinear activation have been demonstrated [18][19][20][21]. However, the hybrid neural networks still have power consumption and latency issues, which are inherent to their reliance on conducting neural computing partially with electronics.…”