Abstract: A method for processor-implemented method includes receiving an integrated circuit (IC) troubleshooting query for an IC. The IC troubleshooting query is received from a user. The method also includes performing natural language processing and machine learning to cluster the IC troubleshooting query into one of a number of semantically similar troubleshooting categories. The method further includes retrieving resolution data from an expert system library, based on a mapping between categories of user solutions and a topic of the IC troubleshooting query. The method also includes generating a recommendation in response to the IC troubleshooting query, based on the resolution data. The method outputs the recommendation to the user.

Abstract: Certain aspects of the present disclosure provide techniques for testing integrated circuit designs based on test cases selected using machine learning models. An example method generally includes receiving a plurality of test cases for an integrated circuit. An embedding data set is generated from the plurality of test cases. A respective embedding for a respective test case of the plurality of test cases generally includes a mapping of the respective test case into a multidimensional space. A plurality of test case clusters is generated based on a clustering model and the embedding data set. A plurality of critical test cases for testing the integrated circuit is selected based on the plurality of test case clusters. The integrated circuit is timed based on the plurality of critical test cases and a hard macro defining the integrated circuit.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for calibrating radio frequency (RF) circuits using machine learning. One example method generally includes calibrating a first subset of RF circuit calibration parameters. Values are predicted for a second subset of RF circuit calibration parameters based on a machine learning model and the first subset of RF circuit calibration parameters. The second subset of RF circuit calibration parameters may be distinct from the first subset of RF circuit calibration parameters. At least the first subset of RF circuit calibration parameters is verified, and after the verifying, at least the first subset of RF circuit calibration parameters are written to a memory associated with the RF circuit.

Abstract: Aspects of the present disclosure relate to wireless communication systems, and in particular, to techniques for generation and processing of an embedding representing a beam for communication. Certain aspects provide a method for wireless communication by a wireless node. The method generally includes receiving an embedding representing a characterization associated with a beam; providing the embedding to a machine learning (ML) model; generating one or more communication parameters for communication using the beam via the ML model based on the embedding; and communicating using the one or more communication parameters.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for calibrating radio frequency (RF) circuits using machine learning. One example method generally includes calibrating a first subset of RF circuit calibration parameters. Values are predicted for a second subset of RF circuit calibration parameters based on a machine learning model and the first subset of RF circuit calibration parameters. The second subset of RF circuit calibration parameters may be distinct from the first subset of RF circuit calibration parameters. At least the first subset of RF circuit calibration parameters is verified, and after the verifying, at least the first subset of RF circuit calibration parameters are written to a memory associated with the RF circuit.

Abstract: Certain aspects of the present disclosure provide techniques for testing integrated circuit designs based on test cases selected using machine learning models. An example method generally includes receiving a plurality of test cases for an integrated circuit. An embedding data set is generated from the plurality of test cases. A respective embedding for a respective test case of the plurality of test cases generally includes a mapping of the respective test case into a multidimensional space. A plurality of test case clusters is generated based on a clustering model and the embedding data set. A plurality of critical test cases for testing the integrated circuit is selected based on the plurality of test case clusters. The integrated circuit is timed based on the plurality of critical test cases and a hard macro defining the integrated circuit.

Abstract: Aspects of the present disclosure relate to wireless communication systems, and in particular, to techniques for generation and processing of an embedding representing a beam for communication. Certain aspects provide a method for wireless communication by a wireless node. The method generally includes receiving an embedding representing a characterization associated with a beam; providing the embedding to a machine learning (ML) model; generating one or more communication parameters for communication using the beam via the ML model based on the embedding; and communicating using the one or more communication parameters.