20230186058. HIGH-RESOLUTION IC NET ROUTING SYSTEM, COMPONENTS AND METHODS WITH DEEP NEURAL NETWORKS simplified abstract (The Board of Trustees of the University of Illinois)

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HIGH-RESOLUTION IC NET ROUTING SYSTEM, COMPONENTS AND METHODS WITH DEEP NEURAL NETWORKS

Organization Name

The Board of Trustees of the University of Illinois

Inventor(s)

Inna Partin-vaisband of Urbana IL (US)

HIGH-RESOLUTION IC NET ROUTING SYSTEM, COMPONENTS AND METHODS WITH DEEP NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20230186058 titled 'HIGH-RESOLUTION IC NET ROUTING SYSTEM, COMPONENTS AND METHODS WITH DEEP NEURAL NETWORKS

Simplified Explanation

The abstract describes a system for obstacle-avoiding pathfinding that uses deep image learning. It explains that a conditional generative adversarial network (CGAN) can be trained to interpret a pathfinding task and map it onto a solution. The system is designed to work efficiently on parallel processing hardware, resulting in a significant speed improvement over traditional methods without any wirelength overhead. The CGAN router can be used to speed up routing and iterative placement in modem integrated circuits (ICs).

  • The system uses deep image learning and a CGAN to solve obstacle-avoiding pathfinding problems.
  • The CGAN is trained to interpret a pathfinding task and generate a solution represented by a bitmap.
  • The system is optimized for parallel processing hardware, such as GPUs, TPUs, or NPUs.
  • It provides a speedup of over an order of magnitude compared to traditional approaches.
  • The system can be used for routing and iterative placement in modem ICs, improving their efficiency.

Potential Applications

The technology described in this patent application has potential applications in various fields, including:

  • Robotics: The system can be used to plan obstacle-avoiding paths for robots, enabling them to navigate complex environments efficiently.
  • Autonomous Vehicles: The technology can help autonomous vehicles plan their routes, avoiding obstacles and optimizing their paths.
  • Video Games: Game developers can utilize the system to create realistic and intelligent pathfinding for non-player characters (NPCs) in virtual worlds.
  • Logistics and Supply Chain: The system can assist in optimizing the movement of goods in warehouses or distribution centers, reducing time and improving efficiency.

Problems Solved

The technology addresses the following problems:

  • Obstacle-Avoiding Pathfinding: The system provides a solution for finding optimal paths while avoiding obstacles in various scenarios.
  • Speed and Efficiency: The system offers a significant speed improvement over traditional approaches, making it suitable for real-time applications.
  • Wirelength Overhead: Unlike other methods, the system does not introduce any wirelength overhead, ensuring efficient use of resources.

Benefits

The technology offers several benefits:

  • Improved Efficiency: The system's parallel processing capabilities and deep image learning enable faster and more efficient pathfinding.
  • Real-Time Performance: The speedup provided by the system makes it suitable for real-time applications, such as robotics and autonomous vehicles.
  • Versatility: The system can be applied to various domains, including robotics, video games, logistics, and more.
  • Resource Optimization: The system does not introduce any wirelength overhead, ensuring efficient use of hardware resources.


Original Abstract Submitted

a multiterminal obstacle-avoiding pathfinding system that utilizes deep image learning. in accordance with the principles herein, a conditional generative adversarial network (cgan) can be trained to interpret a pathfinding task as a graphical bitmap and consequently map a pathfinding problem onto a pathfinding solution represented by another bitmap. due to effective parallelization on parallel processing hardware (such as gpu, tpu, npu, or similar), the system yields over an order of magnitude speedup over traditional approaches with no wirelength overhead. the cgan router can be exploited to significantly speed up routing and iterative placement in modem ics.

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