Various embodiments provide for computational systems including multiple circuit packages, each circuit package comprising an electronic integrated circuit having multiple processing elements and intra-chip bidirectional photonic channels connecting the processing elements into an electro-photonic network, with inter-chip bidirectional photonic channels connecting the processing elements across the electro-photonic networks of the multiple circuit packages into a larger electro-photonic network.

Methods and systems for reverse design of micro-nano structure based on a deep neural network. The method includes step 101 of acquiring initial data of a micro-nano structure according to the micro-nano structure to be reversely designed. The method also includes step 102 of inputting the initial data of the micro-nano structure into a trained optical parameter prediction model to obtain optical prediction parameters. The method further includes step 103 of evaluating the optical prediction parameters. The method also includes optimizing the initial data of the micro-nano structure, inputting the optimized data of the micro-nano structure into the trained optical parameter prediction model, and performing steps 102 and 103 again until the evaluation result of the optical prediction parameters obtained in a current iteration satisfies the preset condition. Through the method of the present application, the electromagnetic response calculation time of the reverse design is greatly shortened.

Methods and systems for reverse design of micro-nano structure based on a deep neural network. The method includes step 101 of acquiring initial data of a micro-nano structure according to the micro-nano structure to be reversely designed. The method also includes step 102 of inputting the initial data of the micro-nano structure into a trained optical parameter prediction model to obtain optical prediction parameters. The method further includes step 103 of evaluating the optical prediction parameters. The method also includes optimizing the initial data of the micro-nano structure, inputting the optimized data of the micro-nano structure into the trained optical parameter prediction model, and performing steps 102 and 103 again until the evaluation result of the optical prediction parameters obtained in a current iteration satisfies the preset condition. Through the method of the present application, the electromagnetic response calculation time of the reverse design is greatly shortened.

Method and system for intelligent decision-making photonic signal processing, where the system comprises a multi-functional input unit, an electro-optical conversion module, a signal processing module, a photoelectric conversion module, a multi-functional output unit, and an artificial intelligence chip. The invention combines the advantages of photonic high-speed, wide-band, and electronic flexibility, combined with heterogeneous photoelectron hybrid integration, packaging and other processes, along with deep learning algorithm, is an intelligent electronic information system that may simultaneously realize digital and analog signal processing.

A pixel feature vector extraction system for extracting multi-scale features contains a cellular neural networks (CNN) based integrated circuit (IC) for extracting pixel feature vector out of input imagery data by performing convolution operations using pre-trained filter coefficients of ordered convolutional layers in a deep learning model. The ordered convolutional layers are organized in a number of groups with each group followed by a pooling layer. Each group is configured for a different size of feature map. Pixel feature vector contains a combination of feature maps from at least two groups, for example, concatenation of the feature maps. The first group of the at least two groups contains the largest size of the feature maps amongst all of the at least two groups. Feature maps of the remaining of the at least two groups are modified to match the size of the feature map of the first group.

A pixel feature vector extraction system for extracting multi-scale features contains a cellular neural networks (CNN) based integrated circuit (IC) for extracting pixel feature vector out of input imagery data by performing convolution operations using pre-trained filter coefficients of ordered convolutional layers in a deep learning model. The ordered convolutional layers are organized in a number of groups with each group followed by a pooling layer. Each group is configured for a different size of feature map. Pixel feature vector contains a combination of feature maps from at least two groups, for example, concatenation of the feature maps. The first group of the at least two groups contains the largest size of the feature maps amongst all of the at least two groups. Feature maps of the remaining of the at least two groups are modified to match the size of the feature map of the first group.

The present disclosure advantageously provides an Optical Hardware Accelerator (OHA) for an Artificial Neural Network (ANN) that includes a communication bus interface, a memory, a controller, and an optical computing engine (OCE). The OCE is configured to execute an ANN model with ANN weights. Each ANN weight includes a quantized phase shift value θ.sub.i and a phase shift value ϕ.sub.i. The OCE includes a digital-to-optical (D/O) converter configured to generate input optical signals based on the input data, an optical neural network (ONN) configured to generate output optical signals based on the input optical signals, and an optical-to-digital (O/D) converter configured to generate the output data based on the output optical signals. The ONN includes a plurality of optical units (OUs), and each OU includes an optical multiply and accumulate (OMAC) module.

An integrated optical circuit for an optical neural network is provided. The integrated optical circuit is configured to process a phase-encoded optical input signal and to provide a phase-encoded output signal depending on the phase-encoded optical input signal. The phase-encoded output signal emulates a synapse functionality with respect to the phase-encoded optical input signal. A related method and a related design structure are further provided.

An integrated optical circuit for an optical neural network is provided. The integrated optical circuit is configured to process a phase-encoded optical input signal and to provide a phase-encoded output signal depending on the phase-encoded optical input signal. The phase-encoded output signal emulates a synapse functionality with respect to the phase-encoded optical input signal. A related method and a related design structure are further provided.

A microscopy system comprises a microscope for analyzing a sample, a computing device for processing measurement signals and at least one microphone for capturing sounds. The computing device is configured to evaluate captured sounds in order to identify a microscope activity in progress or command an intervention in the microscope activity in progress or identify ambient sounds based on microscope sounds.