Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A graphics pipeline includes a cache having cache lines that are configured to store data used to process frames in a graphics pipeline. The graphics pipeline is implemented using a processor that processes frames for the graphics pipeline using data stored in the cache. The processor processes a first frame and writes back a dirty cache line from the cache to a memory concurrently with processing of the first frame. The dirty cache line is retained in the cache and marked as clean subsequent to being written back to the memory. In some cases, the processor generates a hint that indicates a priority for writing back the dirty cache line based on a read command occupancy at a system memory controller.

Abstract: A method for predicting an onset of a capacity fading in a battery includes measuring, over a period of time, a plurality of parameters related to charging and discharging cycles of the battery; detecting, based on the measured plurality of parameters, the onset of the capacity fade in the battery; and providing a notification on the electronic device indicating the detected onset of the capacity fade.

Abstract: Provided are a method and system for determining State of Health (SOH) of a battery. The method includes: detecting a charging state of the battery that is being charged by a Constant Current (CC) or a Constant Voltage (CV); adding a low frequency current signal to the CC that is charging the battery to obtain a low frequency charging current, or adding a low frequency voltage signal to the CV, that is charging the battery to obtain a low frequency charging voltage; determining one or more parameters relating to the battery during the charging state of the battery; calculating an internal impedance and Open Circuit Voltage (OCV) values based on the one or more parameters; and determining SOH of the battery based on the calculated internal impedance and the OCV values.

Abstract: Disclosed are methods and apparatuses for charging a hybrid battery pack, the method including determining, by a battery management system (BMS), a current split ratio for allocating charging current to an energy cell and a power cell in a hybrid battery pack, based on any one or any combination of a state of charge (SoC) level of the energy cell at an instance of initiation of charging, a SoC level of the power cell at the instance of the initiation of the charging, a wattage of an adapter for charging the hybrid battery pack, a capacity of the hybrid battery pack, and a charging time period, and charging, by the BMS, the hybrid battery pack by allocating the charging current to the energy cell and the power cell based on the current split ratio.

Abstract: A graphics pipeline includes a cache having cache lines that are configured to store data used to process frames in a graphics pipeline. The graphics pipeline is implemented using a processor that processes frames for the graphics pipeline using data stored in the cache. The processor processes a first frame and writes back a dirty cache line from the cache to a memory concurrently with processing of the first frame. The dirty cache line is retained in the cache and marked as clean subsequent to being written back to the memory. In some cases, the processor generates a hint that indicates a priority for writing back the dirty cache line based on a read command occupancy at a system memory controller.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: A technique for designing circuits including receiving a data object representing a circuit for a first process technology, the circuit including a first sub-circuit, the first sub-circuit including a first electrical component and a second electrical component arranged in a first topology; identifying the first sub-circuit in the data object by comparing the first topology to a stored topology, the stored topology associated with the first process technology; identifying a first set of physical parameter values associated with first electrical component and the second electrical component of the first sub-circuit; determining a set of performance parameter values for the first sub-circuit based on a first machine learning model of the first sub-circuit and the identified set of physical parameters; converting the identified first sub-circuit to a second sub-circuit for the second process technology based on the determined set of performance parameter values; and outputting the second sub-circuit.

Abstract: The present subject refers to a method for battery fault diagnosis and prevention of hazardous conditions. The method comprises determining a plurality of parameters defined as one or more of current, voltage, or state of charge during operation of a battery-powered device. Further, one or more likelihood ratios related to malfunctioning of the battery are evaluated based on determined parameters. At least one of: a current battery-state or a type of current battery state are determined based on the one or more likelihood ratios as evaluated.

Abstract: Provided are a method and system for determining State of Health (SOH) of a battery. The method includes: detecting a charging state of the battery that is being charged by a Constant Current (CC) or a Constant Voltage (CV); adding a low frequency current signal to the CC that is charging the battery to obtain a low frequency charging current, or adding a low frequency voltage signal to the CV, that is charging the battery to obtain a low frequency charging voltage; determining one or more parameters relating to the battery during the charging state of the battery; calculating an internal impedance and Open Circuit Voltage (OCV) values based on the one or more parameters; and determining SOH of the battery based on the calculated internal impedance and the OCV values.

Abstract: A primary side wireless power transmitter inductively couplable to a secondary side wireless power receiver for supplying power to the wireless power receiver for receiving communications from the secondary side wireless power receiver through the inductive coupling comprises a primary side tank circuit receiving a signal on from the secondary side wireless power receiver. A phase delay or time delay circuit generates a fixed delay clock signal. A sample and hold circuit samples a tank circuit voltage utilizing the fixed phase or time delayed clock signal. A comparator is coupled to an output of the sample and hold circuit for extracting data or commands from the signal stream. A method of operating a primary side wireless transmitter inductively coupled to a secondary side wireless power receiver for supplying power to the wireless power receiver to power a load coupled to the receiver is also disclosed.

Abstract: A method for predicting an onset of a capacity fading in a battery includes measuring, over a period of time, a plurality of parameters related to charging and discharging cycles of the battery; detecting, based on the measured plurality of parameters, the onset of the capacity fade in the battery; and providing a notification on the electronic device indicating the detected onset of the capacity fade.

Abstract: A system and a method for fast charging a battery, based on dynamically determined cut-off voltage are provided. The system includes a hardware processor, and a non-volatile memory comprising instructions, the instructions executed by the hardware processor configure the hardware processor to identify dynamically, a current state of charge (SOC) of the battery, by a battery charging module, determine at least one fast charging profile that matches the current SOC of the battery, by the battery charging module, determine a dynamic cut-off voltage that matches the fast charging profile, by the battery charging module, and charge the battery based on the dynamic cut-off voltage, by the battery charging module.