Optical convolution processor is an optical computing device based on optical principles, optical computing refers to the use of photons (rather than conventional electrons) to perform computing tasks, it uses optical devices and optical signals for calculation, while realizing the integration of computing and transmission. Compared with traditional electronic computers, optical convolutional processors have the advantages of fast computing speed, low energy consumption and strong parallelism. Therefore, it has a wide range of applications in image processing, pattern recognition, neural networks and other fields.
The semiconductor institute has developed an ultra-highly integrated optical convolution processor
The structure of the optical convolution processor includes parts such as optical devices, optical components and control circuits, and the combination and arrangement of these parts determine the function of the optical convolution processor. For example, by adjusting the parameters of optical devices and the signals of control circuits, specific computational tasks such as convolution operations and related operations can be realized. Therefore, the structure and function of optical convolutional processors are closely related, and the optimization of the structure can improve its computing performance and application effect.
Convolutional neural networks
In addition to applications in the field of image processing and pattern recognition, optical convolutional processors are also widely used in the field of artificial intelligence. One typical application is image recognition. By entering the image into the optical convolution processor, its high-speed computing power and parallel processing ability can be used to quickly perform convolution operations on the image, so as to realize the feature extraction and recognition of the image. In addition, optical convolutional processors can also be used to accelerate convolutional neural networks (CNNs) in deep learning to improve the training and inference speed of models.
In addition to image recognition, optical convolutional processors can also be applied to speech recognition, natural language processing, machine translation and other fields. For example, in speech recognition, optical convolution processors can be used to perform convolution operations on acoustic features to improve the accuracy of speech recognition. In natural language processing, optical convolution processors can be used to perform convolution operations on text to extract the characteristics of text, and then achieve tasks such as text classification and sentiment analysis. In machine translation, optical convolution processors can be used to perform convolution operations on sentences in the source and target languages to extract the characteristics of sentences and realize the task of machine translation.
Continental's traditional silicon-based chip manufacturing process has been limited by external forces. The manufacture of silicon photonics chips does not require traditional lithography machines, so there is no restriction. In short, optical convolutional processors are widely used in the field of artificial intelligence, and their high-speed computing power and parallel processing capabilities provide strong support for the development of artificial intelligence.