A method and system for detecting the power consumption of a chip

Abstract

This application relates to the technical field of chip power consumption detection, and provides a method and system for detecting the power consumption of a chip, including obtaining a target infrared image of the target chip under different loads through an infrared thermal imager; calculating the target power consumption value corresponding to the target chip under different loads; making a first comparison between the target power consumption value and the standard power consumption value. If the difference exceeds the first preset range, it is determined that the power consumption of the target chip is abnormal; if the difference is within the first preset range, the target infrared image corresponding to the target power consumption value is compared with the standard infrared image under the current load for a second time; if the result of the second comparison is less than the first threshold, the abnormal area is identified based on the temperature gradient of each pixel point, and the abnormal result is marked on the abnormal area according to the preset judgment strategy. It realizes the acceleration of the chip power consumption detection process, reduces the cost, and also enhances the ability of fault identification, providing strong support for chip quality control.

Description

Technical Field

[0001] This application relates to the technical field of chip power consumption detection. Specifically, it relates to a method and system for detecting the power consumption of a chip. Background Art

[0002] The content of this part only provides background information related to this application, and it may not constitute prior art.

[0003] Chip power consumption detection is a systematic process. It uses high-precision electronic measurement equipment to monitor and record the current and voltage values of the chip in multiple working modes in real time, and then calculates based on these data to accurately evaluate the energy consumption of the chip under different operating conditions. This process is crucial for verifying the energy efficiency of chip design, identifying potential power consumption problems, and optimizing overall performance, aiming to ensure that the chip achieves low power consumption and high efficiency while meeting performance requirements.

[0004] In the prior art, the Chinese patent with the authorization announcement number CN115078965A discloses a method and device, medium, and chip for detecting the temperature field distribution of a chip. The core of this method is to first determine the working temperature target values of multiple key nodes on the chip. These key nodes represent the key areas where heat accumulation or temperature changes may occur in the chip under specific working conditions. Subsequently, by controlling a series of power consumption simulation units, power consumption simulation is carried out one by one according to the working temperature target values corresponding to these key nodes. This process aims to simulate the heat generation situation of the chip in actual operation. To achieve accurate temperature measurement, temperature measurement units are used to sense the temperature conducted from the power consumption simulation units. There is a specific positional relationship between these temperature measurement units and the power consumption simulation units. Based on the sensed temperature data and positional relationship, the position-temperature relationship curve of each power consumption simulation unit can be further generated. This step provides key data for subsequent temperature field distribution analysis. Finally, based on these position-temperature relationship curves, the temperature field distribution of the chip to be measured can be constructed, thus realizing the dynamic positioning of the temperature field based on the working characteristics of the chip.

[0005] However, although this method can theoretically provide accurate detection of the overall temperature field distribution of the chip, and uses the built-in temperature measurement circuit and power consumption simulation circuit as hardware implementation to reduce the dependence on external detection equipment, there are significant defects in actual operation. Specifically, this method requires setting a large number of key nodes on the chip and equipping each node with a corresponding power consumption simulation unit for individual simulation. This process not only greatly increases the complexity of the detection process but also leads to a sharp increase in the number of required devices. Under the combined action of these factors, the detection efficiency is reduced, making this method face great challenges in practical applications. Therefore, how to simplify the detection process, reduce the number of required devices, and improve the detection efficiency while ensuring the detection accuracy has become an urgent problem to be solved currently. Summary of the Invention

[0006] To solve the above technical problems, the purpose of this application is to provide a method and system for detecting the power consumption of a chip. Through infrared imaging technology and neural network prediction, the accuracy and efficiency of chip power consumption detection are effectively improved, and the use of detection equipment is reduced.

[0007] The purpose of this application is achieved through the following technical solutions:

[0008] In the first aspect, the present invention provides a method for detecting the power consumption of a chip, including:

[0009] Obtain target infrared images of the target chip under different loads through an infrared thermal imager;

[0010] Input the target infrared images under different loads into a calculation model to obtain target power consumption values corresponding to the target chip under different loads; the calculation model is constructed based on a neural network model and is trained through a training set and a validation set including infrared images of the target chip under various load conditions and their corresponding actual power consumption data; the training set and the validation set are selected from a data set, and the data set obtains multiple infrared images by performing infrared imaging on the target chip under different load conditions through an infrared thermal imager, and uses a power consumption detector to measure and record the actual power consumption data corresponding to each infrared image;

[0011] Perform a first comparison between the target power consumption value and the standard power consumption value. If the difference exceeds the first preset range, it is determined that the power consumption of the target chip is abnormal; if the difference is within the first preset range, then compare the target infrared image corresponding to the target power consumption value with the standard infrared image under the current load; if the result of the second comparison is that the similarity is greater than or equal to the first threshold, it is determined that the power consumption of the target chip is qualified; if the result of the second comparison is less than the first threshold, then identify the abnormal area where the temperature change exceeds the second preset range based on the temperature gradient of each pixel point of the target infrared image, and mark the abnormal result on the abnormal area according to the preset judgment strategy;

[0012] The judgment strategy includes: if the difference between the temperature of the abnormal area and the temperature of the surrounding area is positive and the difference is greater than the second threshold, mark the abnormal area as a short circuit; if the difference between the temperature of the abnormal area and the temperature of the surrounding area is negative and the absolute value of the difference is greater than the second threshold, mark the abnormal area as an open circuit.

[0013] Further, the step of obtaining the target infrared image of the target chip under different loads by an infrared thermal imager specifically includes:

[0014] Calibrate the infrared thermal imager;

[0015] Perform preheating treatment on the target chip under different load conditions;

[0016] Under each load condition, obtain multiple infrared images and perform averaging processing to obtain the target infrared image to reduce random errors.

[0017] Further, the step of identifying the abnormal area where the temperature change exceeds the second preset range based on the temperature gradient of each pixel point of the target infrared image specifically includes:

[0018] Use the abnormal point recognition model to identify the target infrared image to obtain the coordinate data of the target abnormal points; the abnormal point recognition model includes: use the Sobel operator to calculate the temperature gradients of the target infrared image in the x direction and the y direction respectively, calculate the gradient magnitude and gradient direction of each pixel point according to the temperature gradients, if the gradient magnitude of any pixel point exceeds the preset third threshold, mark it as a possible abnormal point; detect the difference in the gradient directions between the possible abnormal points and the adjacent pixel points, if the gradient direction difference exceeds the preset angle threshold, mark it as a target abnormal point;

[0019] Check other pixel points within the preset neighborhood around each target abnormal point, if there are other abnormal points within the preset neighborhood, merge the target abnormal point and the other abnormal points within the preset neighborhood into the same abnormal area.

[0020] Further, the expression of the abnormal point recognition model is:

[0021]

[0022] Among them, and respectively represent the gradients of the point on the target image in the and directions, is the preset third threshold, is the preset angle threshold, is the adjacent pixel point at and respectively represent the point In and the gradient in the direction.

[0023] Furthermore, after marking the abnormal result on the abnormal area according to the preset judgment strategy, it further includes:

[0024] Obtain the topology graph of the target chip as a reference graph;

[0025] According to the position information on the topology graph, map the abnormal area and its corresponding abnormal result to the topology graph; mark the position of the abnormal area on the topology graph in a visual way and display the abnormal result.

[0026] Furthermore, the judgment strategy further includes:

[0027] If the difference between the temperature of the abnormal area and the temperature of the surrounding area is less than the second threshold, and the temperature of the abnormal area has periodic fluctuations, then mark it as an overheated or poor heat dissipation area.

[0028] Furthermore, the second comparison adopts one of the structural similarity index comparison or histogram comparison.

[0029] In a second aspect, the present invention provides a detection system for chip power consumption, including:

[0030] A target infrared image acquisition module, configured to acquire target infrared images of the target chip under different loads through an infrared thermal imager;

[0031] A target power consumption value calculation module, configured to input the target infrared images under different loads into a calculation model to obtain the target power consumption values corresponding to the target chip under different loads; the calculation model is constructed based on a neural network model and trained through a training set and a validation set including infrared images of the target chip under various load conditions and their corresponding actual power consumption data; the training set and the validation set are selected from a data set, and the data set acquires multiple infrared images by infrared imaging of the target chip under different load conditions through an infrared thermal imager, and uses a power consumption detector to measure and record the actual power consumption data corresponding to each infrared image;

[0032] An abnormal recognition module, configured to perform a first comparison between the target power consumption value and a standard power consumption value. If the difference exceeds a first preset range, it is determined that the power consumption of the target chip is abnormal; if the difference is within the first preset range, then compare the target infrared image corresponding to the target power consumption value with the standard infrared image under the current load for a second comparison; if the result of the second comparison is that the similarity is greater than or equal to a first threshold, it is determined that the power consumption of the target chip is qualified; if the result of the second comparison is less than the first threshold, then identify an abnormal area where the temperature change exceeds a second preset range based on the temperature gradient of each pixel point of the target infrared image, and mark the abnormal result on the abnormal area according to the preset judgment strategy;

[0033] The determination strategy includes: if the difference between the temperature of the abnormal area and the temperature of the surrounding area is positive and the difference is greater than the second threshold, then mark the abnormal area as a short circuit; if the difference between the temperature of the abnormal area and the temperature of the surrounding area is negative and the absolute value of the difference is greater than the second threshold, then mark the abnormal area as an open circuit.

[0034] In a third aspect, the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps corresponding to the method in the first aspect are implemented.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps corresponding to the method in the first aspect are implemented.

[0036] In summary, the technical solutions of the embodiments of the present application at least have the following advantages and beneficial effects:

[0037] The present invention uses an infrared thermal imager to capture infrared images of a target chip under different load states. These images reflect the heat distribution of the chip, and thus indirectly reflect the power consumption state of the chip. Subsequently, these infrared images are input into a neural network model that has been pre-trained with a training set and a validation set containing infrared images of the target chip under various load conditions and their measured power consumption data. The model can calculate the power consumption values of the target chip under different loads accordingly. This process not only realizes the rapid detection of power consumption but also significantly reduces the dependence on professional power consumption detection instruments, thereby reducing the detection cost. By comparing the calculated target power consumption value with the standard power consumption value and analyzing the similarity between the target infrared image and the standard infrared image, it is possible to accurately determine whether the power consumption of the chip is abnormal. If the power consumption is abnormal and there is an area with abnormal temperature change in the infrared image, according to the temperature gradient of the abnormal area and the temperature difference from the surrounding area, identify and mark the possible short circuit or open circuit faults of the chip, thereby improving the detection efficiency and accuracy. It realizes the acceleration of the chip power consumption detection process, reduces the cost, and enhances the ability of fault identification, providing strong support for chip quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of a method for detecting the power consumption of a chip provided by the present invention;

[0039] Figure 2 is a schematic structural diagram of a system for detecting the power consumption of a chip provided by the present invention;

[0040] Figure 3 is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, rather than all, of the embodiments of this application. The components of the embodiments of this application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0042] A method for detecting the power consumption of a chip proposed in an embodiment of this application includes:

[0043] S101. Obtain a target infrared image of the target chip under different loads through an infrared thermal imager; specifically, first obtain an infrared image of the surface temperature distribution through the infrared thermal imager. The primary link in this process is to strictly calibrate the infrared thermal imager, which is a key step to ensure that the infrared image obtained in subsequent analysis can accurately reflect the actual temperature distribution of the target chip. The calibration work usually includes adjusting parameters such as the sensitivity, resolution, and temperature range of the thermal imager to match the temperature measurement requirements of the target chip, thereby eliminating the influence of instrument errors on the accuracy of temperature measurement.

[0044] Immediately afterwards, perform a preheating process on the target chip under different load conditions. Its purpose is to make the temperature distribution of the chip reach a relatively stable state and reduce measurement errors caused by temperature fluctuations. The specific time and method of the preheating process need to be determined according to the chip characteristics and load conditions to ensure that the chip can reach a relatively constant temperature field before formal measurement.

[0045] Under each preset load condition, perform the acquisition work of infrared images. To improve the reliability of the data and reduce random errors, a strategy of taking multiple shots for each load state is adopted, and these images are averaged. The averaging process can smooth out the random noise in the image, improve the accuracy of temperature measurement, and thus obtain the target infrared image under this load condition. Ensuring the high quality and representativeness of the image data used in subsequent analysis lays a solid foundation for accurately calculating the power consumption value.

[0046] S102. Input the target infrared images under different loads into a calculation model to obtain the target power consumption values corresponding to the target chip under different loads; the calculation model is constructed based on a neural network model and is trained through a training set and a validation set containing infrared images of the target chip under various load conditions and their corresponding actual power consumption data; the training set and the validation set are selected from a data set, and the data set obtains multiple infrared images by performing infrared imaging on the target chip under different load conditions through an infrared thermal imager, and uses a power consumption detector to measure and record the actual power consumption data corresponding to each infrared image.

[0047] Specifically, the infrared image is used as the key input data and fed into a pre-constructed computational model. This computational model is designed based on a neural network architecture, which has the advantage of being able to handle complex non-linear relationships and has the ability to extract key features from the input image to predict the target power consumption value. The training process of the model is crucial. It relies on a large dataset obtained through designed experiments: using an infrared thermal imager to perform infrared imaging on the target chip under various different load conditions, thus capturing multiple infrared images reflecting the surface temperature distribution of the chip. At the same time, a power consumption detector is used to accurately measure and record the actual power consumption data corresponding to each infrared image. In this way, each pair of infrared image and power consumption data constitutes a training sample.

[0048] To ensure the accuracy and generalization ability of the model, the dataset is further divided into a training set and a validation set. The training set is used for the initial learning and parameter adjustment of the model, while the validation set is used to evaluate the performance of the model and prevent overfitting. Through multiple iterative trainings, the weights and biases of the neural network are continuously adjusted until the performance of the model on the validation set reaches the optimal, and it can accurately map the infrared image to the corresponding power consumption value.

[0049] The successful implementation of this step depends not only on high-quality input data, but also on the rationality of the model design and the rigor of the training process. Once the model is trained, it can efficiently predict the power consumption value of the target chip under different load conditions from new infrared images, providing strong support for subsequent power consumption evaluation and anomaly detection.

[0050] In practical applications, a model based on convolutional neural network (CNN) is used for power consumption prediction. CNN has excellent performance in the field of image processing and can automatically extract key features in images. To train this CNN model, a dataset containing infrared images of the target chip under various load conditions and their corresponding actual power consumption data is prepared. Specifically, 1000 infrared images under different loads are used as the training set, and these images cover the temperature distribution of the chip in various working states. At the same time, the actual power consumption data corresponding to each image is measured using a power consumption detector and recorded. To verify the performance of the model, 200 infrared images are prepared as the validation set.

[0051] During the training process, the following key parameters were set: The network architecture adopted a CNN model containing multiple convolutional layers and fully connected layers. The convolutional layers were used to extract local features in the images, while the fully connected layers were used to combine these features for power consumption prediction. The learning rate was set to 0.001 to ensure the learning speed of the model while avoiding overfitting. The batch size was set to 32, which means that in each iteration, the model would process 32 images simultaneously. A sufficient number of iterations (e.g., 100 times) were set to ensure that the model could fully learn the data in the training set.

[0052] During the training process, the cross-entropy loss function was used to measure the difference between the model's predicted values and the actual power consumption values, and the weights and biases of the model were updated through the backpropagation algorithm. As the training progressed, the performance of the model on the validation set gradually stabilized and finally reached the required accuracy.

[0053] S103, make a first comparison between the target power consumption value and the standard power consumption value. If the difference exceeds the first preset range, it is determined that the power consumption of the target chip is abnormal; if the difference is within the first preset range, then make a second comparison between the target infrared image corresponding to the target power consumption value and the standard infrared image under the current load; if the result of the second comparison is that the similarity is greater than or equal to the first threshold, it is determined that the power consumption of the target chip is qualified; if the result of the second comparison is less than the first threshold, then identify the abnormal area where the temperature change exceeds the second preset range based on the temperature gradient of each pixel point of the target infrared image, and mark the abnormal result on the abnormal area according to the preset judgment strategy; the judgment strategy includes: if the difference between the temperature of the abnormal area and the temperature of the surrounding area is positive and the difference is greater than the second threshold, mark the abnormal area as a short circuit; if the difference between the temperature of the abnormal area and the temperature of the surrounding area is negative and the absolute value of the difference is greater than the second threshold, mark the abnormal area as an open circuit. In addition, if the difference between the temperature of the abnormal area and the temperature of the surrounding area is less than the second threshold, it means that there is no problem with the power consumption of the target chip itself, and if the temperature of this abnormal area has periodic fluctuations, it means that there is a problem with heat dissipation at this position, so it is marked as an overheated or poor heat dissipation area.

[0054] Among them, the first comparison aims to quickly screen out chips with significantly abnormal power consumption values. If the difference between the target power consumption value and the standard power consumption value exceeds the first preset range set in advance, it can be directly determined that the power consumption of the target chip is abnormal, and there is no need to conduct a detailed analysis of the subsequent steps. However, if the difference between the target power consumption value and the standard power consumption value is within the first preset range, that is, it is initially judged that the power consumption value is within the normal range, then it is necessary to further check the power consumption status of the chip through image comparison. At this time, the target infrared image corresponding to the target power consumption value is compared with the standard infrared image under the current load condition for the second comparison. The second comparison uses one of the structural similarity index comparison or histogram comparison. The power consumption status of the chip is evaluated by comparing the similarity between the two. The basis for judging the similarity is the preset first threshold. If the similarity between the target infrared image and the standard infrared image is greater than or equal to the first threshold, it indicates that the power consumption distribution of the chip is consistent with the standard state, and it is determined that the power consumption of the target chip is qualified.

[0055] If the result of the second comparison shows that the similarity is less than the first threshold, it indicates that there is an abnormality in the power consumption distribution of the chip, and it is necessary to further analyze the abnormal area. At this time, using the temperature gradient of each pixel point in the target infrared image, identify the abnormal area where the temperature change exceeds the second preset range. For these abnormal areas, conduct a detailed analysis and marking according to the preset judgment strategy. The judgment strategy mainly includes two situations: one is when the temperature of the abnormal area is higher than the surrounding area and the temperature difference is greater than the second threshold, it is determined that there may be a short circuit problem in the abnormal area; the other is when the temperature of the abnormal area is lower than the surrounding area and the absolute value of the temperature difference is greater than the second threshold, it is determined that there may be an open circuit problem in the abnormal area. Through such a judgment strategy, the power consumption abnormal area of the chip can be accurately located, and the corresponding abnormal result marking can be given, providing strong support for subsequent fault troubleshooting and repair.

[0056] Among them, the step of identifying the abnormal area where the temperature change exceeds the second preset range based on the temperature gradient of each pixel point in the target infrared image includes: using the abnormal point recognition model to identify the target infrared image to obtain the coordinate data of the target abnormal points; the abnormal point recognition model includes: using the Sobel operator to calculate the temperature gradients of the target infrared image in the x direction and the y direction respectively, calculating the gradient magnitude and gradient direction of each pixel point according to the temperature gradients, if the gradient magnitude of any pixel point exceeds the preset third threshold, then mark it as a possible abnormal point; detecting the difference in the gradient direction between the possible abnormal point and the adjacent pixel points, if the gradient direction difference exceeds the preset angle threshold, then mark it as a target abnormal point.

[0057] Check other pixel points within the preset neighborhood around each target abnormal point. If there are other abnormal points within the preset neighborhood, merge the target abnormal point and the other abnormal points within the preset neighborhood into the same abnormal area.

[0058] Specifically, the target infrared image is first processed using the Sobel operator to calculate the temperature gradient of the image in the x-direction and y-direction. The Sobel operator is a discrete differential operator that can effectively extract edge information in an image and is used here to capture the gradient of temperature change. By calculating the gradient amplitude and gradient direction of each pixel, the intensity and directionality of the temperature change can be quantified. The specific calculation process is as follows:

[0059]

[0060] in, for The temperature value at for The temperature value at for The temperature value at for The temperature value at and Points Temperature gradient in the x-direction and the y-direction.

[0061] Gradient Amplitude for:

[0062]

[0063] Gradient direction for

[0064]

[0065] If the gradient amplitude of a pixel exceeds the preset third threshold , the point is initially marked as a possible abnormal point, which means that the temperature change at this point is relatively drastic, and may hide power consumption abnormalities. That is:

[0066]

[0067] Subsequently, in order to further improve the accuracy of recognition, the possible abnormal points marked initially are further verified. By detecting the gradient direction difference between the possible abnormal point and the adjacent pixel point, if the difference exceeds the preset angle threshold, the point is confirmed to be a true abnormal point, that is, the target abnormal point. This step is intended to eliminate those misjudged points caused by noise or error, and ensure that the identified abnormal points have significant temperature gradient changes, and this change has a certain degree of consistency in direction. The specific calculation formula is as follows:

[0068]

[0069] in, is the gradient direction difference between the possible abnormal point and adjacent pixel points, is the gradient direction of the adjacent pixel point, is the adjacent pixel point, is the angle threshold.

[0070] Combined with formulas (1) to (7), the expression of the abnormal point recognition model is:

[0071]

[0072] Among them, and respectively represent the gradients of the point on the target image in the and directions, is the preset third threshold, is the preset angle threshold, is the adjacent pixel point at, and respectively represent the gradients of the point on the target image in the and directions.

[0073] After determining the target abnormal points, regional merging processing is also required. Check other pixel points in the preset neighborhood around each target abnormal point. If there are other marked abnormal points in the neighborhood, these abnormal points are merged into a unified abnormal area. This helps to identify the specific location of the power consumption anomaly and can integrate those adjacent but independent abnormal points to form a more complete and accurate description of the abnormal area.

[0074] Based on the same inventive concept, the present invention provides a detection system for chip power consumption, including:

[0075] A target infrared image acquisition module 201, configured to acquire a target infrared image of a target chip under different loads through an infrared thermal imager.

[0076] A target power consumption value calculation module 202, configured to input the target infrared images under different loads into a calculation model to obtain the target power consumption values corresponding to the target chip under different loads; the calculation model is constructed based on a neural network model and is trained through a training set and a validation set including infrared images of the target chip under various load conditions and their corresponding actual power consumption data; the training set and the validation set are selected from a data set, and the data set acquires multiple infrared images by performing infrared imaging on the target chip under different load conditions through an infrared thermal imager, and measures and records the actual power consumption data corresponding to each infrared image using a power consumption detector.

[0077] Anomaly recognition module 203 is used to make a first comparison between the target power consumption value and the standard power consumption value. If the difference exceeds the first preset range, it is determined that the power consumption of the target chip is abnormal; if the difference is within the first preset range, the target infrared image corresponding to the target power consumption value is compared with the standard infrared image under the current load for a second time; if the result of the second comparison is that the similarity is greater than or equal to the first threshold, it is determined that the power consumption of the target chip is qualified; if the result of the second comparison is less than the first threshold, an abnormal area where the temperature change exceeds the second preset range is identified based on the temperature gradient of each pixel point of the target infrared image, and an abnormal result is marked on the abnormal area according to the preset judgment strategy;

[0078] The judgment strategy includes: if the difference between the temperature of the abnormal area and the temperature of the surrounding area is positive and the difference is greater than the second threshold, mark the abnormal area as a short circuit; if the difference between the temperature of the abnormal area and the temperature of the surrounding area is negative and the absolute value of the difference is greater than the second threshold, mark the abnormal area as an open circuit.

[0079] Based on the same inventive concept, the present invention provides an electronic device, including: a memory 302, a processor 301, and a computer program stored on the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, a method for detecting the power consumption of a chip is implemented.

[0080] Based on the same inventive concept, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, a method for detecting the power consumption of a chip is implemented.

[0081] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting chip power consumption, characterized in that: include: Obtain target infrared images of the target chip under different loads through an infrared thermal imager; The target infrared images under different loads are input into a calculation model to obtain the target power consumption values ​​corresponding to the target chip under different loads; the calculation model is constructed based on a neural network model, and is trained by a training set and a validation set containing infrared images of the target chip under various load conditions and their corresponding actual power consumption data; the training set and the validation set are obtained by screening a data set, and the data set is obtained by performing infrared imaging of the target chip under different load conditions by an infrared thermal imager to obtain multiple infrared images, and using a power consumption detector to measure and record the actual power consumption data corresponding to each infrared image; Performing a first comparison between the target power consumption value and the standard power consumption value, and if the difference exceeds a first preset range, determining that the power consumption of the target chip is abnormal; If the difference is within the first preset range, performing a second comparison between the target infrared image corresponding to the target power consumption value and the standard infrared image under the current load; If the result of the second comparison is that the similarity is greater than or equal to the first threshold, the power consumption of the target chip is determined to be qualified; if the result of the second comparison is less than the first threshold, an abnormal area where the temperature change exceeds a second preset range is identified based on the temperature gradient of each pixel point of the target infrared image, and an abnormal result is marked on the abnormal area according to a preset judgment strategy; The judgment strategy includes: if the difference between the temperature of the abnormal area and the temperature of the surrounding area is positive, and the difference is greater than a second threshold, then the abnormal area is marked as a short circuit; if the difference between the temperature of the abnormal area and the temperature of the surrounding area is negative, and the absolute value of the difference is greater than the second threshold, then the abnormal area is marked as an open circuit; The step of identifying an abnormal area where the temperature change exceeds a second preset range based on the temperature gradient of each pixel point of the target infrared image specifically includes: The target infrared image is identified by using an outlier recognition model to obtain coordinate data of the target outlier point; the outlier recognition model includes: using the Sobel operator to calculate the temperature gradient of the target infrared image in the x direction and the y direction respectively, and calculating the gradient amplitude and gradient direction of each pixel point according to the temperature gradient, and if the gradient amplitude of any pixel point exceeds a preset third threshold, it is marked as a possible outlier point; detecting the gradient direction difference between the possible outlier point and the adjacent pixel point, and if the gradient direction difference exceeds a preset angle threshold, it is marked as a target outlier point; Check other pixel points in a preset neighborhood around each target abnormal point. If there are other abnormal points in the preset neighborhood, merge the target abnormal point and other abnormal points in the preset neighborhood into the same abnormal area.

2. A chip power consumption detection method according to claim 1, characterized in that: The step of obtaining the target infrared image of the target chip under different loads by using the infrared thermal imager specifically includes: Calibrate the infrared thermal imager; Preheating the target chip under different load conditions; Under each load condition, multiple infrared images are acquired and averaged to obtain the target infrared image to reduce random errors.

3. The method for detecting chip power consumption according to claim 1, characterized in that: The expression of the outlier recognition model is: middle, and Represent the points on the target image exist and The gradient in direction, is the preset third threshold, is the preset angle threshold, for Neighboring pixels, and Represent the points on the target image exist and Directional gradient.

4. The method for detecting chip power consumption according to claim 1, characterized in that: After marking the abnormal result on the abnormal area according to the preset judgment strategy, the method further includes: Acquire a topological map of the target chip as a reference map; According to the position information on the topological map, the abnormal area and its corresponding abnormal result are mapped to the topological map; the position of the abnormal area is marked in a visual manner on the topological map, and the abnormal result is displayed.

5. The method for detecting chip power consumption according to claim 1, characterized in that: The judgment strategy also includes: If the difference between the temperature of the abnormal area and the temperature of the surrounding area is less than a second threshold, and the temperature of the abnormal area fluctuates periodically, it is marked as an overheated or poorly heat-dissipating area.

6. The method for detecting chip power consumption according to claim 1, characterized in that: The second comparison adopts one of structural similarity index comparison and histogram comparison.

7. A chip power consumption detection system, characterized in that: include: A target infrared image acquisition module is used to acquire target infrared images of the target chip under different loads through an infrared thermal imager; A target power consumption value calculation module is used to input the target infrared images under different loads into a calculation model to obtain the target power consumption values ​​corresponding to the target chip under different loads; the calculation model is constructed based on a neural network model, and is trained by a training set and a verification set containing infrared images of the target chip under various load conditions and their corresponding actual power consumption data; the training set and the verification set are obtained by screening a data set, and the data set is obtained by performing infrared imaging of the target chip under different load conditions by an infrared thermal imager to obtain multiple infrared images, and using a power consumption detector to measure and record the actual power consumption data corresponding to each infrared image; an abnormality identification module, used for first comparing the target power consumption value with the standard power consumption value, and if the difference exceeds a first preset range, determining that the power consumption of the target chip is abnormal; If the difference is within the first preset range, performing a second comparison between the target infrared image corresponding to the target power consumption value and the standard infrared image under the current load; If the result of the second comparison is that the similarity is greater than or equal to the first threshold, the power consumption of the target chip is determined to be qualified; if the result of the second comparison is less than the first threshold, an abnormal area where the temperature change exceeds a second preset range is identified based on the temperature gradient of each pixel point of the target infrared image, and an abnormal result is marked on the abnormal area according to a preset judgment strategy; The judgment strategy includes: if the difference between the temperature of the abnormal area and the temperature of the surrounding area is positive, and the difference is greater than a second threshold, marking the abnormal area as a short circuit; If the difference between the temperature of the abnormal area and the temperature of the surrounding area is negative, and the absolute value of the difference is greater than a second threshold, the abnormal area is marked as a short circuit.

8. An electronic device, characterized in that: The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, steps corresponding to the method for detecting chip power consumption according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps corresponding to the method for detecting chip power consumption as described in any one of claims 1 to 6 are implemented.

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