Abstract: Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network.

Abstract: Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. The methods can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC); performing a thermal simulation for each respective template of the IC based on a sequence of power patterns of tiles of the respective template; and training a neural network with a plurality of training data collected via thermal simulations performed for the templates of the IC. These systems and methods can use a machine learning predictor, that has been trained to determine a transient temperature rise across an entire IC, and then append the determined transient temperature rise to a system level thermal profile of the IC.

Abstract: Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network.

Abstract: Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network.

Abstract: Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network.

Abstract: Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. The methods can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC); performing a thermal simulation for each respective template of the IC based on a sequence of power patterns of tiles of the respective template; and training a neural network with a plurality of training data collected via thermal simulations performed for the templates of the IC. These systems and methods can use a machine learning predictor, that has been trained to determine a transient temperature rise across an entire IC, and then append the determined transient temperature rise to a system level thermal profile of the IC.

Abstract: Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network.

Abstract: Data is received that characterizes a chip in the package system (CPS) having a plurality of wires and vias. Thereafter, using the received data, a chip power calculation is performed. The chip power calculated is used to generate a thermal-aware power map. Further, package and system level thermal analysis is performed using the power map to generate a tile-based CPS thermal profile. A plurality of chip finite element sub-models are then generated that each correspond to a different tile. A thermal field solution is solved for each sub-model so that, for each wire, wire temperature rises are extracted from the corresponding the chip sub-model analysis and combined with temperature values from the CPS thermal profile. This extracting and combining is then used to generate a back-annotation file covering each metal wire and via in the CPS.

Abstract: Data is received that characterizes a chip in the package system (CPS) having a plurality of wires and vias. Thereafter, using the received data, a chip power calculation is performed. The chip power calculated is used to generate a thermal-aware power map. Further, package and system level thermal analysis is performed using the power map to generate a tile-based CPS thermal profile. A plurality of chip finite element sub-models are then generated that each correspond to a different tile. A thermal field solution is solved for each sub-model so that, for each wire, wire temperature rises are extracted from the corresponding the chip sub-model analysis and combined with temperature values from the CPS thermal profile. This extracting and combining is then used to generate a back-annotation file covering each metal wire and via in the CPS.

Abstract: Data is received that characterizes a chip in the package system (CPS) having a plurality of wires and vias. Thereafter, using the received data, a chip power calculation is performed. The chip power calculated is used to generate a thermal-aware power map. Further, package and system level thermal analysis is performed using the power map to generate a tile-based CPS thermal profile. A plurality of chip finite element sub-models are then generated that each correspond to a different tile. A thermal field solution is solved for each sub-model so that, for each wire, wire temperature rises are extracted from the corresponding the chip sub-model analysis and combined with temperature values from the CPS thermal profile. This extracting and combining is then used to generate a back-annotation file covering each metal wire and via in the CPS.