Abstract: An integrated clock gate (ICG) includes an OR-AND-INVERT gate to receive a first enable and a second enable; a first inverter coupled to the output of the OR-AND-INVERT; a first NAND gate coupled to the output of the first inverter; a second NAND gate coupled to the output of the OR-AND-INVERT; and a second inverter to provide a clock which is gated based on logic values of the first enable and/or the second enable, wherein an output of the second inverter is received as input by the OR-AND-INVERT-gate. The ICG circuit reduces capacitance of input clk pin, which translates to lower switching power when clock is gated and reduction in dynamic power of clock network, since buffers in clock tree driving the ICG cells can be downsized. The ICG cell has the smallest transistor count (and area) when compared to existing ICG cell topologies.

Abstract: Embodiments herein relate to optimizing the duration of a sense amp enable signal in a memory device such as SRAM. A control circuit asserts the sense amp enable signal in response to a clock signal from a replica column of the SRAM. A feedback path extends from the sense amps back to the control circuit. In one approach, a change in a feedback signal on the feedback path indicates the sense amps have all received the sense amp enable signal. In another approach, a change in a feedback signal on the feedback path indicates the sense amps have all completed their sensing operations. In some cases, a selection can be made among multiple feedback paths.

Abstract: A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.

Abstract: A robotic process automation (RPA) workflow may be automatically created from text, an image, and/or a media file. A workflow sequence may be converted into a digital format using optical character recognition (OCR), and this information may then be analyzed by an artificial intelligence (AI) model and converted into a predicted RPA workflow. The predicted RPA workflow may be presented to a developer for approval, denial, or modification. Information pertaining to the selection by the developer may then be used for subsequent retraining of the AI model to improve prediction accuracy.

Abstract: Training and using artificial intelligence (AI)/machine learning (ML) models to automatically supplement and/or complete code of RPA workflows is disclosed. A trained AI/ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The AI/ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the AI/ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

Abstract: Automatic completion of robotic process automation (RPA) workflows using machine learning (ML) is disclosed. A trained ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

Abstract: A system and a computer-implemented method for analyzing a robotic process automation (RPA) workflow are disclosed herein. The computer-implemented method may include obtaining the RPA workflow and analyzing the obtained RPA workflow to provide an analyzed RPA workflow. The computer-implemented method may further include determining one or metrics associated with the analyzed RPA workflow and performing one or more corrective activities for the analyzed RPA workflow based on the determined one or more metrics.

Abstract: A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.

Abstract: A robotic process automation (RPA) workflow may be automatically created from text, an image, and/or a media file. A workflow sequence may be converted into a digital format using optical character recognition (OCR), and this information may then be analyzed by an artificial intelligence (AI) model and converted into a predicted RPA workflow. The predicted RPA workflow may be presented to a developer for approval, denial, or modification. Information pertaining to the selection by the developer may then be used for subsequent retraining of the AI model to improve prediction accuracy.

Abstract: Automatic completion of robotic process automation (RPA) workflows using machine learning (ML) is disclosed. A trained ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.