Method and system for timing optimization with detour prediction
First Claim
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1. A method implemented in an electronic design automation system, comprising the steps of:
- performing a routing operation to generate routing and detour location data for a chip;
generating a data model in response to a machine learning process that describes a relationship between an image map of the chip and the detour location data;
determining predicted detour locations based on the data model and the image map;
determining the probability of detouring in a region of the predicted detour locations;
determining a predicted detour net for a path in a region having a high probability of detour;
determining sensitivity of predicted detour nets along the path; and
routing a detour net for the chip in response to the determined sensitivity of the predicted detour nets, whereinthe step of generating the data model further comprises;
receiving the routing and detour location data;
receiving a map of circuit element placement derived from placement data; and
converting the map into the image map, andthe map includes a congestion map, and the step of converting further comprises;
obtaining information on the number of nets available in a region each in a first direction and in a second direction different from the first direction; and
obtaining information on the number of nets actually routed in the region each in the first direction and in the second direction.
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Abstract
A method for timing optimization is disclosed. The method includes obtaining information on detour locations of a chip by performing a routing operation, establishing, through machine learning, a model that describes a relationship between an image map and the detour locations, generating predicted detour locations based on the model and the image map, determining the probability of detouring in a region of the predicted detour locations, determining a predicted detour net for a path in a region having a high probability of detour, and determining sensitivity of the path.
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Citations
17 Claims
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1. A method implemented in an electronic design automation system, comprising the steps of:
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performing a routing operation to generate routing and detour location data for a chip; generating a data model in response to a machine learning process that describes a relationship between an image map of the chip and the detour location data; determining predicted detour locations based on the data model and the image map; determining the probability of detouring in a region of the predicted detour locations; determining a predicted detour net for a path in a region having a high probability of detour; determining sensitivity of predicted detour nets along the path; and routing a detour net for the chip in response to the determined sensitivity of the predicted detour nets, wherein the step of generating the data model further comprises; receiving the routing and detour location data; receiving a map of circuit element placement derived from placement data; and converting the map into the image map, and the map includes a congestion map, and the step of converting further comprises; obtaining information on the number of nets available in a region each in a first direction and in a second direction different from the first direction; and obtaining information on the number of nets actually routed in the region each in the first direction and in the second direction. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method implemented in an electronic design automation system, comprising:
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performing a placement operation to generate placement data of a chip; determining the number of nets available for a global routing cell in a map of the chip including information on the placement data; determining the number of nets actually routed for the global routing cell in the map including information on the placement data; converting the chip map into a grayscale image based on information on the number of available nets and the number of actually routed nets; feeding the converted chip map to a machine learning system; and routing a detour net for the chip in response to the machine learning system processing the converted chip map, wherein the step of converting further comprises; determining a grayscale value of a pixel in the chip map based on the number of actually routed nets in a vertical direction and the number of available nets in the vertical direction, and generating a first image map; determining a grayscale value of a pixel in the chip map based on the number of actually routed nets in a horizontal direction and the number of available nets in the horizontal direction, and generating a second image map; sorting the chip map by pitch, resulting in a number of pitch maps; and converting each of the pitch maps into a grayscale image. - View Dependent Claims (12, 13, 14)
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15. An electronic design automation system, comprising one or more processors and one or more programs including instructions which, when executed by the one or more processors, cause the system to:
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perform a routing operation to generate placement data of a chip; generate a data model in response to a machine learning process that describes a relationship between an image map of the chip and the detour locations; generate predicted detour locations based on the data model and the chip image map; determine the probability of detouring in a region of the predicted detour locations; determine a predicted detour net for a path in a region having a high probability of detour; determine sensitivity of predicted detour nets along the path; and route a detour net for the chip in response to the determined sensitivity of the predicted detour nets, wherein the generating of the data model further comprising; receiving the routing and detour location data; receiving a map of circuit element placement derived from placement data; and converting the map into the image map, and the map includes a congestion map, and the converting of the map further comprising; obtaining information on the number of nets available in a region each in a first direction and in a second direction different from the first direction; and obtaining information on the number of nets actually routed in the region each in the first direction and in the second direction. - View Dependent Claims (16, 17)
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Specification