Abstract: A configuration control circuit of a flash-type FPGA capable of suppressing programming interference is provided. The configuration control circuit adds a programming selection circuit compared with a conventional configuration control circuit. When a programming operation is performed on a flash memory cell located in a target row and a target column, the programming selection circuit controls a path between a programming bit line (BL) voltage and a BL voltage obtaining terminal of the flash memory cell located in the target row and the target column to be turned on, and a path between the programming BL voltage and a BL voltage obtaining terminal of a flash memory cell located in another row and the target column to be turned off.

Abstract: A field-programmable gate array (FPGA) for using a configuration shift chain to implement a multi-bitstream function includes a bitstream control circuit, a multi-bitstream configuration shift chain and a configurable module. The FPGA enables multi-bitstream storage configuration bits to latch configuration bitstreams by adjusting a circuit structure of a multi-bitstream configuration shift chain in a combination of a control logic of a bitstream control circuit for the multi-bitstream configuration shift chain, and outputs one latched configuration bitstream from a configuration output terminal to a configurable module through each multi-bitstream storage configuration bit as required, so that the configurable module implements a logic function corresponding to the configuration bitstream outputted by the multi-bitstream configuration shift chain.

Abstract: Provided are a method and an apparatus for debugging, and a system on chip. The method for debugging includes: a component to be debugged receives a debugging instruction from a controller, and the component to be debugged performs debugging operation according to the debugging instruction and configuration of a state machine inside the component to be debugged. Then an SW level debugging operation of component on system on chip can be achieved, which improves the debugging efficiency of these components with large amounts of data flow on system on chip.

Abstract: A field-programmable gate array (FPGA) for using a configuration shift chain to implement a multi-bitstream function includes a bitstream control circuit, a multi-bitstream configuration shift chain and a configurable module. The FPGA enables multi-bitstream storage configuration bits to latch configuration bitstreams by adjusting a circuit structure of a multi-bitstream configuration shift chain in a combination of a control logic of a bitstream control circuit for the multi-bitstream configuration shift chain, and outputs one latched configuration bitstream from a configuration output terminal to a configurable module through each multi-bitstream storage configuration bit as required, so that the configurable module implements a logic function corresponding to the configuration bitstream outputted by the multi-bitstream configuration shift chain.

Abstract: Provided are a scalable data fusion method and related products. The scalable data fusion method is applied in a central device and includes: receiving sensing data transmitted by each of M first edge devices, wherein M is an integer equal to or greater than 1; fusing the sensing data transmitted by each of the M first edge devices to obtain M pieces of fused data respectively corresponding to the M first edge devices; distributing the M pieces of fused data to the M first edge devices respectively; receiving object information transmitted by each of the M first edge devices, wherein the object information is obtained based on the fused data; and integrating the object information transmitted by each of the M first edge devices and construct surrounding information based on the integrated object information.

Abstract: Provided are a proposals processing method and related products for object detection, where the method comprising: determining a score threshold according to a statistics property of scores of a first quantity of proposal; selecting a second quantity of proposals with a score higher than the score threshold from the first quantity of proposals; and suppressing to the second quantity of proposals to get a third quantity of non-overlapping proposals with high scores, as such, time complexity for selecting a second quantity of proposals is reduced, and efficiency for selection of the proposals to be used for suppression is improved, thereby improving proposal processing efficiency and object detection efficiency.

Abstract: The present disclosure provides a method for checking a to-be-checked signal and related products. The method is applied in a checking device and includes: a first obtaining module, configured to obtain a to-be-checked signal carrying first control information, wherein the first control information is generated based on original control information; a second obtaining module, configured to obtain original checking information; a determining module, configured to determine the first control information according to the to-be-checked signal; and a checking module, configured to check correctness of the first control information according to the original checking information. The present disclosure can be used to enable reliability and functional safety on devices originally designed without features intended to support those functions.

Abstract: Embodiments of methods, apparatuses, devices and systems associated with one or more representative images are disclosed.

Abstract: Systems and methods for determining semantically related terms using an active learning framework such as Transductive Experimental Design are disclosed. Generally, to enhance a keyword suggestion tool, an active learning module trains a model to predict whether a term is relevant to a user. The model is then used to present the user with terms that have been determined to be relevant based on the model so that an online advertisement service provider may more efficiently provide a user with terms that are semantically related to a seed set.

Abstract: Described are a system and method for selecting a training sample from a sample set. The method comprises determining proximities between all data samples in a set of the data samples, forming edges between the data samples as a function of the proximities, computing weights for the edges as a function of the proximities, selecting a plurality of the data samples as a function of the weights to form a subset of the data samples, and storing the subset of the data samples.

Abstract: This disclosure describes systems and methods for categorizing web pages. Web pages and terms selected from those web pages are organized in a matrix. The number of terms in the matrix are filtered using a Laplacian score algorithm. A linear regression algorithm or some other algorithm may use the filtered set of terms to fit the web pages into pre-defined categories.

Abstract: Systems and methods for determining semantically related terms using an active learning framework such as Transductive Experimental Design are disclosed. Generally, to enhance a keyword suggestion tool, an active learning module trains a model to predict whether a term is relevant to a user. The model is then used to present the user with terms that have been determined to be relevant based on the model so that an online advertisement service provider may more efficiently provide a user with terms that are semantically related to a seed set.

Abstract: Embodiments of methods, apparatuses, devices and systems associated with one or more representative images are disclosed.

Abstract: A system is described for classifying a search query. The system may create a machine learning classifier function that may be “trained” by a plurality of categorized queries within a query taxonomy. The system may represent the queries as term vectors and input the term vectors to the machine learning classifier function to generate a value that may correspond to a particular category within the query taxonomy. The system may regularize the machine learning classifier function based on user search click data to improve the classifying accuracy.

Abstract: This disclosure describes systems and methods for categorizing web pages. Web pages and terms selected from those web pages are organized in a matrix. The number of terms in the matrix are filtered using a Laplacian score algorithm. A linear regression algorithm or some other algorithm may use the filtered set of terms to fit the web pages into pre-defined categories.

Abstract: Described are a system and method for selecting a training sample from a sample set. The method comprises determining proximities between all data samples in a set of the data samples, forming edges between the data samples as a function of the proximities, computing weights for the edges as a function of the proximities, selecting a plurality of the data samples as a function of the weights to form a subset of the data samples, and storing the subset of the data samples.

Abstract: Systems and methods for determining semantically related terms using an active learning framework such as Transductive Experimental Design are disclosed. Generally, to enhance a keyword suggestion tool, an active learning module trains a model to predict whether a term is relevant to a user. The model is then used to present the user with terms that have been determined to be relevant based on the model so that an online advertisement service provider may more efficiently provide a user with terms that are semantically related to a seed set.

Abstract: The present invention is directed towards systems and methods for generating a linear regression model based on statistically frequent query pairs. The method of the present invention comprises storing statistically frequent query pairs, the query pairs constituting a query and a query rewrite. Query pair samples are generated based on the statistically frequent query pairs and an active learning algorithm is utilized to select the most informative query pairs. A linear regression algorithm is then utilized to generate a linear regression model based on the selected most informative query pairs.

Abstract: A system is described for classifying a search query. The system may create a machine learning classifier function that may be “trained” by a plurality of categorized queries within a query taxonomy. The system may represent the queries as term vectors and input the term vectors to the machine learning classifier function to generate a value that may correspond to a particular category within the query taxonomy. The system may regularize the machine learning classifier function based on user search click data to improve the classifying accuracy.

Abstract: A method and system for determining relatedness of images of pages based on link and page layout analysis. A link analysis system determines relatedness between images by first identifying blocks within web pages, and then analyzing the importance of the blocks to web pages, web pages to blocks, and images to blocks. Based on this analysis, the link analysis system determines the degree to which each image is related to each other image. The link analysis system may also use the relatedness of images to generate a ranking of the images. The link analysis system may also generate a vector representation of the images based on their relatedness and apply a clustering algorithm to the vector representations to identify clusters of related images.