Reconfigurable Diffraction General Intelligent Optical Computing Method, Architecture and System

Abstract

The present disclosure relates to the field of optical computing technologies, and particularly to a reconfigurable diffraction general intelligent optical computing method, architecture, and system. The method includes: obtaining a first matrix corresponding to an input mode, and obtaining a signal parameter matrix through a modulator array; obtaining a second matrix of input parameters, and processing the second matrix through a modulation kernel to obtain a corresponding modulation parameter matrix; randomly fusing the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix; inputting the fusion matrix into a target diffraction propagation model to obtain a diffraction transmission matrix; and outputting a target-form signal based on the diffraction transmission matrix. The present disclosure realizes the arbitrary reconfigurability of the diffraction calculation result through the dynamic adjustment of the modulation parameters and the random fusion of the modulation parameter matrix and the signal parameter matrix, and further, by spatially separating the diffraction calculation and the reconfigurable modulation parameters, the diffraction calculation is enabled to have general computing capabilities, breaking through the challenge of difficult diffraction network reconstruction.

Description

Technical Field

[0001] The present disclosure relates to the field of optical computing technology, and in particular, to a reconfigurable diffraction general intelligent optical computing method, architecture and system. Background Art

[0002] With the rapid development of the fields of artificial intelligence and scientific computing, the complexity and scale of computing requirements are also increasing continuously. However, existing microelectronic computing chips face performance bottlenecks (such as speed, energy consumption, etc.) when dealing with the rapidly growing computing power requirements, and it is difficult to effectively meet the increasingly stringent requirements of large-scale complex algorithms for computing power and power consumption. As a new computing paradigm, optical computing utilizes the propagation characteristics and parallel processing capabilities of light, showing the advantages of high computing power and low energy consumption, making optical computing technology regarded as the key to breaking the existing computing bottleneck. Summary of the Invention

[0003] The present disclosure aims to at least partly solve one of the technical problems in the related art.

[0004] To this end, the first object of the present disclosure is to propose a reconfigurable diffraction general intelligent optical computing method.

[0005] The second object of the present disclosure is to propose a reconfigurable diffraction general intelligent optical computing system.

[0006] To achieve the above object, an embodiment of the first aspect of the present disclosure proposes a reconfigurable diffraction general intelligent optical computing method, the method comprising:

[0007] Obtaining a first matrix corresponding to an input modality, and obtaining a signal parameter matrix through a modulator array;

[0008] Obtaining a second matrix of input parameters, and processing the second matrix through a modulation kernel to obtain a corresponding modulation parameter matrix;

[0009] Randomly fusing the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix;

[0010] Inputting the fusion matrix into a target diffraction propagation model to obtain a diffraction transmission matrix;

[0011] Outputting a target form signal based on the diffraction transmission matrix.

[0012] Optionally, the processing the second matrix through a modulation kernel to obtain a corresponding modulation parameter matrix includes:

[0013] Determining a parameter type corresponding to the input parameter;

[0014] Based on the parameter type, determining a target modulation parameter corresponding to the modulation kernel;

[0015] Based on the target modulation parameters, the second matrix is processed through a modulation kernel to obtain a corresponding modulation parameter matrix.

[0016] Optionally, the randomly fusing the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix includes: juxtaposing or cross-fusing the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix.

[0017] Optionally, the inputting the fusion matrix into a target diffraction propagation model to obtain a diffraction transmission matrix includes: inputting the fusion matrix into a target diffraction propagation model, and obtaining a diffraction transmission matrix through a target diffraction kernel in the target diffraction propagation model, where the target diffraction kernel is trained.

[0018] Optionally, the outputting a target form signal based on the diffraction transmission matrix includes:

[0019] Determining whether the diffraction transmission matrix meets the output condition;

[0020] If the diffraction transmission matrix meets the output condition, determining the target form to be output;

[0021] Based on the target form, determining a corresponding signal feeding technique;

[0022] Based on the signal feeding technique and the diffraction transmission matrix, outputting a target form signal.

[0023] Optionally, the method further includes:

[0024] If the diffraction transmission matrix does not meet the output condition, adjusting the parameters of the modulation kernel based on the diffraction transmission matrix;

[0025] Based on the diffraction transmission matrix, repeating the above steps until the diffraction transmission matrix meets the output condition, and outputting a target form signal.

[0026] To achieve the above object, a second aspect embodiment of the present disclosure proposes a reconfigurable diffraction general intelligent optical computing system, and the system includes:

[0027] A signal feeding module, configured to obtain a first matrix corresponding to an input mode, and obtain a signal parameter matrix through a modulator array;

[0028] A parameter modulation module, configured to obtain a second matrix of input parameters, and process the second matrix through a modulation kernel to obtain a corresponding modulation parameter matrix;

[0029] A channel synthesis module, configured to randomly fuse the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix;

[0030] A diffraction propagation module, configured to input the fusion matrix into a target diffraction propagation model to obtain a first diffraction transmission matrix;

[0031] A result output module, configured to output a target form signal based on the first diffraction transmission matrix.

[0032] Another object of the present disclosure is to provide a reconfigurable diffraction general intelligent optical computing architecture, including: at least one of the reconfigurable diffraction general intelligent optical computing systems.

[0033] In summary, the reconfigurable diffraction general intelligent optical computing method and system provided by the present disclosure achieve the arbitrary reconfigurability of the diffraction calculation results through the dynamic adjustment of modulation parameters and the random fusion of the modulation parameter matrix and the signal parameter matrix. Moreover, by spatially separating the diffraction calculation and the reconfiguring modulation parameters, the diffraction calculation has general computing capabilities, breaking through the challenge of difficult reconfiguration of the diffraction network.

[0034] Some of the additional aspects and advantages of the present disclosure will be given in the following description, some will become apparent from the following description, or will be understood through the practice of the present disclosure. Description of the Drawings

[0035] The above and / or additional aspects and advantages of the present disclosure will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0036] Figure 1 is a schematic flowchart of a reconfigurable diffraction general intelligent optical computing method provided by an embodiment of the present disclosure;

[0037] Figure 2 is a schematic diagram of a reconfigurable diffraction general intelligent optical computing method provided by an embodiment of the present disclosure;

[0038] Figure 3 is a schematic diagram of a reconfigurable diffraction general intelligent optical computing method provided by an embodiment of the present disclosure;

[0039] Figure 4 is a schematic structural diagram of a reconfigurable diffraction general intelligent optical computing system provided by an embodiment of the present disclosure. Detailed Embodiments

[0040] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0041] At present, diffractive neural networks can combine neural networks with ultra-high-density optical diffraction, demonstrating the advantages of ultra-high computing power and ultra-low energy consumption. However, existing diffractive optical computing chips cannot be reconfigured for different application scenarios and computing requirements, lacking flexibility in structure. Thus, the problem of reconfiguration in diffractive computing has led to the problem of single computing functions in diffractive neural networks, greatly limiting their application scenarios and practical value.

[0042] Moreover, after the diffractive network chip is fabricated, its structure or optical properties cannot be arbitrarily changed, resulting in a very single chip architecture and function, making it difficult to effectively meet the requirements of multi-functional and multi-scenario applications, and greatly restricting the practical application value of the chip. In the prior art, on-chip reconfigurable technologies are explored and optimized at the level of photonic devices (such as photonic devices based on waveguide structures), such as non-volatile regulation based on phase change materials, transient regulation technologies based on plasma dispersion effects and thermo-optical effects, etc. In the prior art, for laser direct writing all-optical reconfiguration technology, the size mismatch between neurons and the reconfiguration means (laser spot) leads to the problem of reconfiguration crosstalk between high-density neurons, and the high dependence of the reconfiguration process on the microscopic imaging system and the high-precision mechanical displacement system determines the dual limitation of the reconfiguration scale and reconfiguration efficiency; or for electro-optic regulation technology based on plasma dispersion and thermo-optical effects, the severe dependence of the reconfiguration effect (such as resolution and scale) on the electrode size and complex manufacturing process directly weakens its application value. Based on this, the current on-chip reconfiguration technology cannot well solve the problems of crosstalk and low reconfiguration efficiency in high-density reconfiguration of sub-wavelength scale neurons, and it is particularly difficult to accurately reconfigure sub-wavelength scale diffractive neurons.

[0043] The following will describe the present disclosure in detail with specific embodiments.

[0044] Figure 1 It is a schematic flowchart of a reconfigurable diffractive general intelligent optical computing method provided by an embodiment of the present disclosure. As Figure 1 shown, the reconfigurable diffractive general intelligent optical computing method may include the following steps:

[0045] Step 101, obtain a first matrix corresponding to the input modality, and obtain a signal parameter matrix through a modulator array;

[0046] Step 102, obtain a second matrix of input parameters, and process the second matrix through a modulation check to obtain a corresponding modulation parameter matrix;

[0047] Step 103, randomly fuse the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix;

[0048] Step 104, input the fusion matrix into a target diffractive propagation model to obtain a diffractive transmission matrix;

[0049] Step 105: Output a target-form signal based on the diffraction transfer matrix.

[0050] In one embodiment of the present disclosure, the above-mentioned reconfigurable diffraction general intelligent optical computing method is applicable to different tasks, such as image classification and multi-modal information recognition.

[0051] Among them, in one embodiment of the present disclosure, the above-mentioned input modality can be set as needed. For example, it can be an image, text, or audio.

[0052] In one embodiment of the present disclosure, a signal parameter matrix corresponding to the first matrix can be obtained through a modulator array. Among them, in one embodiment of the present disclosure, the above-mentioned signal parameter matrix can be an amplitude and / or phase matrix corresponding to the first matrix.

[0053] Moreover, in one embodiment of the present disclosure, the second matrix of the above-mentioned input parameters can be obtained by processing the required parameters through the corresponding modulator array. Among them, in one embodiment of the present disclosure, the above-mentioned required parameter can be a voltage.

[0054] Furthermore, in one embodiment of the present disclosure, the method of processing the second matrix through a modulation kernel to obtain a corresponding modulation parameter matrix may include the following steps:

[0055] Step 1021: Determine the parameter type corresponding to the input parameter;

[0056] Step 1022: Determine the target modulation parameter corresponding to the modulation kernel based on the parameter type;

[0057] Step 1023: Process the second matrix through the modulation kernel based on the target modulation parameter to obtain a corresponding modulation parameter matrix.

[0058] Among them, in one embodiment of the present disclosure, the parameter type corresponding to the input parameter can be determined manually or based on the type of the input parameter.

[0059] Moreover, in one embodiment of the present disclosure, the above-mentioned modulation kernel can be trained, and different parameter types correspond to different target modulation parameters. Among them, in one embodiment of the present disclosure, the implementation method of the above-mentioned modulation kernel may include but is not limited to spatial light or on-chip, and includes but is not limited to thermal modulators, carrier modulators, or phase change materials, etc., and includes but is not limited to specific material platforms, such as silicon, silicon dioxide, silicon nitride, lithium niobate, etc.

[0060] Further, in an embodiment of the present disclosure, after obtaining the target modulation parameter through the above steps, the second matrix can be processed based on the target modulation parameter through modulation to obtain the corresponding modulation parameter matrix.

[0061] Among them, in an embodiment of the present disclosure, after obtaining the modulation parameter matrix and the signal parameter matrix through the above steps, the modulation parameter matrix and the signal parameter matrix can be randomly fused to obtain the corresponding fusion matrix. In an embodiment of the present disclosure, the modulation parameter matrix and the signal parameter matrix can be randomly fused as needed, so as to achieve the arbitrary reconfigurable characteristics of the diffraction calculation result.

[0062] In addition, in an embodiment of the present disclosure, the method of randomly fusing the modulation parameter matrix and the signal parameter matrix to obtain the fusion matrix may include: juxtaposing or cross-fusing the modulation parameter matrix and the signal parameter matrix to obtain the fusion matrix.

[0063] Further, in an embodiment of the present disclosure, after obtaining the fusion matrix through the above steps, the fusion matrix can be input into the target diffraction propagation model to obtain the diffraction transmission matrix. Among them, in an embodiment of the present disclosure, the method of inputting the fusion matrix into the target diffraction propagation model to obtain the diffraction transmission matrix may include: inputting the fusion matrix into the target diffraction propagation model, and obtaining the diffraction transmission matrix through the target diffraction kernel in the target diffraction propagation model, where the target diffraction kernel is trained.

[0064] In addition, in an embodiment of the present disclosure, the specific implementation manner of the above diffraction kernel may include but is not limited to single-layer and multi-layer diffraction, and the form of neurons may include but is not limited to neurons of various shapes, such as rectangles, circles, cones, ellipses, etc. Among them, in an embodiment of the present disclosure, the corresponding target diffraction kernel can be obtained through training corresponding to the diffraction kernel to determine the implementation manner corresponding to the target diffraction kernel and the form of neurons.

[0065] Further, in an embodiment of the present disclosure, after obtaining the diffraction transmission matrix through the above steps, a target form signal can be output based on the diffraction transmission matrix. Specifically, in an embodiment of the present disclosure, the method of outputting a target form signal based on the diffraction transmission matrix may include the following steps:

[0066] Step 1051, determine whether the diffraction transmission matrix meets the output condition;

[0067] Step 1052, if the diffraction transmission matrix meets the output condition, determine the target form to be output;

[0068] Step 1053, based on the target form, determine the corresponding signal feeding technology;

[0069] Step 1054: Output a target - form signal based on the signal feeding - out technology and the diffraction transmission matrix.

[0070] Among them, in an embodiment of the present disclosure, the above - mentioned output condition may be the number of loop calculations.

[0071] In addition, in an embodiment of the present disclosure, when the above - mentioned reconfigurable diffraction - based general intelligent optical computing method is applied to different tasks, the corresponding output conditions are also different. For example, in an embodiment of the present disclosure, assuming that the reconfigurable diffraction - based general intelligent optical computing method is applied to image classification, the corresponding output condition may be one loop calculation; assuming that the reconfigurable diffraction - based general intelligent optical computing method is applied to multi - modal information recognition, the corresponding output condition may be four loop calculations.

[0072] Furthermore, in an embodiment of the present disclosure, if the diffraction transmission matrix meets the output condition, the target form to be output can be determined. Among them, in an embodiment of the present disclosure, the target form to be output can be determined by user input, and the target form includes but is not limited to waveguide signals, optical radiation signals, and electrical signals, including but not limited to the intensity and spatial distribution of optical signals, or the voltage and current of electrical signals.

[0073] In addition, in an embodiment of the present disclosure, after determining the target form through the above - mentioned steps, the corresponding signal feeding - out technology can be determined according to the target form, so as to output a target - form signal based on the signal feeding - out technology and the diffraction transmission matrix.

[0074] In an embodiment of the present disclosure, the signal feeding - out technologies corresponding to different target forms are also different.

[0075] Specifically, in an embodiment of the present disclosure, the signal feeding - out technology corresponding to the waveguide signal may be a mode converter based on a waveguide array; the signal feeding - out technology corresponding to the optical radiation signal may be an optical radiator based on a coupler array; the signal feeding - out technology corresponding to the electrical signal may be an optoelectronic converter based on an optoelectronic detector array.

[0076] Furthermore, in an embodiment of the present disclosure, the above - mentioned method may further include the following steps:

[0077] Step 1055: If the diffraction transmission matrix does not meet the output condition, adjust the parameters of the modulation kernel in the parameter modulation module based on the diffraction transmission matrix;

[0078] Step 1056: Based on the diffraction transmission matrix, repeat the above - mentioned steps until the diffraction transmission matrix meets the output condition, and then output a target - form signal.

[0079] Among them, in an embodiment of the present disclosure, after adjusting the parameters of the modulation kernel in the parameter modulation module based on the diffraction transfer matrix, the diffraction transfer matrix can be used as the first matrix and the second matrix, and the above steps are repeated to obtain an updated diffraction transfer matrix until the updated diffraction transfer matrix meets the output conditions, and a target form signal is output.

[0080] In an embodiment of the present disclosure, the above steps can be based on the dynamic modulation characteristics of the modulation kernel and realize the dynamic adjustment of the target modulation parameters according to the feedback of the output diffraction transfer matrix.

[0081] Figure 2 and Figure 3 is a schematic diagram of a reconfigurable diffraction general intelligent optical computing method provided by an embodiment of the present disclosure.

[0082] As Figure 2 shown, after the signal parameter matrix and the modulation parameter matrix are randomly fused to obtain a fusion matrix, the fusion matrix is input into the target diffraction propagation model, the diffraction transfer matrix is obtained through the diffraction kernel in the target diffraction propagation model, and the modulation parameters in the modulation kernel are adjusted through the diffraction transfer matrix, and then the above steps are repeated through the diffraction transfer matrix to obtain a target form signal.

[0083] As Figure 3 shown, after adjusting the modulation parameters in the modulation kernel through the diffraction transfer matrix, corresponding modulation kernels 1, 2, 3 until modulation kernel N can be generated respectively, and the target form signal is obtained through the corresponding modulation kernel and diffraction kernel in sequence.

[0084] In summary, the reconfigurable diffraction general intelligent optical computing method provided in this embodiment realizes the arbitrary reconfigurable characteristics of the diffraction calculation results through the dynamic adjustment of the modulation parameters and the random fusion of the modulation parameter matrix and the signal parameter matrix, and through separating the diffraction calculation and the reconfiguring modulation parameters in space, so that the diffraction calculation has general computing capabilities and breaks through the challenge of difficult diffraction network reconstruction.

[0085] To implement the above embodiment, Figure 4 A reconfigurable diffraction general intelligent optical computing system provided by an embodiment of the present disclosure, the system includes:

[0086] A signal feeding module 401, configured to obtain a first matrix corresponding to the input mode and obtain a signal parameter matrix through the modulator array;

[0087] A parameter modulation module 402, configured to obtain a second matrix of the input parameters and process the second matrix through the modulation kernel to obtain a corresponding modulation parameter matrix;

[0088] The channel synthesis module 403 is used to randomly fuse the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix;

[0089] The diffraction propagation module 404 is used to input the fusion matrix into the target diffraction propagation model to obtain a first diffraction transmission matrix;

[0090] The result output module 405 is used to output a target-form signal based on the first diffraction transmission matrix.

[0091] Among them, there does not have to be an obvious boundary in form between the signal feeding port and the parameter modulation port of the diffraction module. They can be the same or different. Therefore, in the network design, their spatial distribution will be added to the training as part of the parameters.

[0092] Among them, in one embodiment of the present disclosure, there does not have to be an obvious boundary in form between the signal feeding module port and the parameter modulation module port of the above diffraction propagation module. They can be the same or different. Based on this, in the network design, their spatial distribution will be added to the training as part of the parameters.

[0093] Moreover, in one embodiment of the present disclosure, the essence of the optical device or on-chip reconstruction technology of the chip is the interaction between light and matter. For the currently widely used laser direct writing reconstruction technology, plasma dispersion effect modulation technology, and thermo-optical effect modulation technology, their core differences lie in the energy carrier, action object, action mode, and action effect of the light-matter interaction. Based on this, in one embodiment of the present disclosure, the proposed reconfigurable diffraction general intelligent optical computing system adopts a new modular reconstruction idea, and during the reconstruction process, it focuses on the regulation effect of external modulation parameters on the diffraction propagation output result, and realizes the predetermined regulation effect through the optimal deployment of regulation parameters.

[0094] Furthermore, in one embodiment of the present disclosure, the ablation of the channel boundary between the signal input and the parameter input is essentially the same. Considering the challenges faced by traditional regulation technologies in regulating dense diffraction neurons, the embodiments of the present disclosure intend to make a more flexible selection of the signal and parameter input channels of the reconfigurable diffraction general intelligent optical computing system, and do not pay attention to the specific form of the regulation energy, so as to be expected to have better compatibility with current various regulation technologies.

[0095] Optionally, in one embodiment of the present disclosure, the above parameter modulation module 402 is specifically used for:

[0096] Determine the parameter type corresponding to the input parameter;

[0097] Based on the parameter type, determine the target modulation parameter corresponding to the modulation kernel;

[0098] Based on the target modulation parameter, process the second matrix through the modulation kernel to obtain the corresponding modulation parameter matrix.

[0099] Optionally, in one embodiment of the present disclosure, the above-mentioned channel synthesis module 403 is specifically configured to:

[0100] Perform juxtaposed fusion or cross-fusion on the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix.

[0101] Optionally, in one embodiment of the present disclosure, the above-mentioned diffraction propagation module 404 is specifically configured to:

[0102] Input the fusion matrix into a target diffraction propagation model, and obtain a diffraction transmission matrix through a target diffraction kernel in the target diffraction propagation model, where the target diffraction kernel is trained.

[0103] Optionally, in one embodiment of the present disclosure, the above-mentioned result output module 405 is specifically configured to:

[0104] Determine whether the diffraction transmission matrix meets the output condition;

[0105] If the diffraction transmission matrix meets the output condition, determine the target form to be output;

[0106] Based on the target form, determine the corresponding signal feeding technique;

[0107] Based on the signal feeding technique and the diffraction transmission matrix, output a signal in the target form.

[0108] Optionally, in one embodiment of the present disclosure, the above-mentioned device is further configured to:

[0109] If the diffraction transmission matrix does not meet the output condition, adjust the parameters of the modulation kernel based on the diffraction transmission matrix;

[0110] Based on the diffraction transmission matrix, repeat the above steps until the diffraction transmission matrix meets the output condition, and output a signal in the target form.

[0111] The present disclosure also proposes a reconfigurable diffraction general intelligent optical computing architecture, which may include: at least one of the above-mentioned reconfigurable diffraction general intelligent optical computing systems, and cascading the reconfigurable diffraction general intelligent optical computing systems to complete corresponding tasks.

[0112] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0113] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of such legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the users, including but not limited to notifying the users to read the user agreement / user notice before using the function and signing an agreement / authorization including authorizing relevant user information. In addition, any necessary steps should be taken to defend and safeguard access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0114] The present disclosure anticipates embodiments that can provide users with the option to block the use or access of personal information data. That is, the present disclosure anticipates that hardware and / or software can be provided to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the users.

[0115] In the technical solution of the present disclosure, the acquisition, transmission, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0116] It should be noted that in an embodiment of the present disclosure, certain industry-existing solutions such as software, components, models, etc. may be mentioned. They should be considered exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of the present application, but it does not mean that the applicant has already or necessarily used this solution.

[0117] In the description of the foregoing embodiments, the descriptions referring to terms such as "an embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0118] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of such features. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0119] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present disclosure includes additional implementations where functions may be executed not in the order shown or discussed, including in a substantially simultaneous manner according to the associated functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present disclosure pertain.

[0120] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing a logical function and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which a program can be printed, as the program can be obtained, for example, electronically by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0121] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0122] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0123] In addition, in each of the various embodiments of the present disclosure, the functional units can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0124] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A reconfigurable diffraction universal intelligent optical computing method, characterized in that: The method comprises: Obtain a first matrix corresponding to the input mode, and obtain a signal parameter matrix through the modulator array; Acquire a second matrix of input parameters, and process the second matrix through a modulation core to obtain a corresponding modulation parameter matrix, wherein a parameter type corresponding to the input parameter is determined, and based on the parameter type, a target modulation parameter corresponding to the modulation core is determined, and based on the target modulation parameter, the second matrix is ​​processed through the modulation core to obtain a corresponding modulation parameter matrix; The modulation parameter matrix and the signal parameter matrix are randomly fused to obtain a fused matrix, wherein the modulation parameter matrix and the signal parameter matrix are fused in parallel or cross-fused to obtain a fused matrix; Inputting the fusion matrix into the target diffraction propagation model to obtain a diffraction transmission matrix; Based on the diffraction transmission matrix, a target form signal is output.

2. The method according to claim 1, characterized in that: The step of inputting the fusion matrix into the target diffraction propagation model to obtain the diffraction transmission matrix includes: inputting the fusion matrix into the target diffraction propagation model, and obtaining the diffraction transmission matrix through the target diffraction kernel in the target diffraction propagation model, wherein the target diffraction kernel is trained.

3. The method according to claim 1, characterized in that The outputting of a target form signal based on the diffraction transmission matrix comprises: Determining whether the diffraction transfer matrix meets an output condition; If the diffraction transfer matrix meets the output condition, determining the target form to be output; Based on the target form, determining a corresponding signal feeding technology; Based on the signal feeding technology and the diffraction transmission matrix, a target form signal is output.

4. The method according to claim 3, characterized in that: The method further comprises: If the diffraction transfer matrix does not meet the output condition, adjusting the parameters of the modulation kernel based on the diffraction transfer matrix; Based on the diffraction transfer matrix, the above steps are repeated until the diffraction transfer matrix meets the output conditions and the target form signal is output.

5. A reconfigurable diffraction universal intelligent optical computing system, characterized in that: The system comprises: A signal feeding module is used to obtain a first matrix corresponding to an input mode and obtain a signal parameter matrix through a modulator array; A parameter modulation module, used for acquiring a second matrix of input parameters, and processing the second matrix through a modulation core to obtain a corresponding modulation parameter matrix, wherein a parameter type corresponding to the input parameter is determined, and based on the parameter type, a target modulation parameter corresponding to the modulation core is determined, and based on the target modulation parameter, the second matrix is ​​processed through the modulation core to obtain a corresponding modulation parameter matrix; A channel synthesis module, used for randomly fusing the modulation parameter matrix and the signal parameter matrix to obtain a fusion matrix, wherein the modulation parameter matrix and the signal parameter matrix are fused in parallel or cross-fused to obtain a fusion matrix; A diffraction propagation module, used for inputting the fusion matrix into a target diffraction propagation model to obtain a first diffraction transmission matrix; The result output module is used to output a target form signal based on the first diffraction transmission matrix.

6. A reconfigurable diffraction universal intelligent optical computing architecture, characterized in that: include: At least one reconfigurable diffractive universal intelligent optical computing system as claimed in claim 5.

Images