OrthoRoute: GPU-Accelerated PCB Autorouter

Project Overview

OrthoRoute is a GPU-accelerated PCB autorouter, designed for massively parallel routing of complex circuit boards. Unlike traditional CPU-based autorouters that process nets sequentially, OrthoRoute leverages thousands of CUDA cores to route all nets simultaneously.

Key Features:

True parallel routing using GPU compute
Real-time visualization of routing process
Orthogonal grid-based routing strategy
Open source and KiCad integration
Optimized for high-density, complex boards

Technical Architecture

Core Innovation: Parallel Pathfinding

One CUDA thread per net - simultaneous routing of thousands of connections
Shared grid state - dynamic congestion management across all threads
Conflict resolution - real-time negotiation between competing routes

Grid-Based Routing Strategy

Orthogonal grid on multiple PCB layers
Via sites at grid intersections for layer changes
Capacity management - multiple traces per grid segment
Adaptive fragmentation - reuse partial grid segments

Project Structure

OrthoRoute/
├── src/
│   ├── core/
│   │   ├── grid_generator.py       # Grid infrastructure
│   │   ├── netlist_parser.py       # KiCad integration
│   │   └── route_optimizer.py      # Quality metrics
│   ├── cuda/
│   │   ├── routing_kernels.cu      # Parallel A* pathfinding
│   │   ├── conflict_resolution.cu  # Grid conflict handling
│   │   └── memory_manager.cu       # GPU memory optimization
│   ├── visualization/
│   │   ├── realtime_display.py     # OpenGL rendering
│   │   ├── route_animator.py       # Routing visualization
│   │   └── performance_monitor.py  # Statistics dashboard
│   └── kicad_plugin/
│       ├── orthoroute_plugin.py    # KiCad Action Plugin
│       └── ui_controls.py          # GUI integration
├── tests/
│   ├── unit_tests/
│   ├── benchmark_boards/           # Test PCBs
│   └── performance_tests/
├── docs/
│   ├── algorithm_design.md
│   ├── cuda_optimization.md
│   └── user_guide.md
└── examples/
    ├── simple_board/
    ├── high_density_board/
    └── hypercube_backplane/        # Original use case

Development Phases

Phase 1: Proof of Concept (Week 1)

Goal: Route simple boards with basic parallel algorithm

Core grid generation and CUDA data structures
Basic A* pathfinding kernel (single layer)
KiCad netlist import and board geometry extraction
Simple visualization with matplotlib
Unit tests for small boards (10-100 nets)

Deliverable: Working prototype that can route simple boards faster than traditional tools

Phase 2: Scaling & Optimization (Week 2)

Goal: Handle complex boards with thousands of nets

Multi-layer routing with via placement
Conflict resolution algorithms for competing nets
Memory optimization for large datasets
Real-time OpenGL visualization
Performance benchmarking vs existing tools

Deliverable: Production-ready autorouter for complex boards

Phase 3: Integration & Polish (Week 3)

Goal: Professional KiCad integration and documentation

KiCad Action Plugin with GUI controls
Route quality optimization (wire length, via count)
Comprehensive documentation and examples
Performance tuning and edge case handling
Community release preparation

Deliverable: Public release of OrthoRoute as open source project

Target Performance

Benchmark Comparisons

Hardware Requirements

Minimum: RTX 3060 (3,584 CUDA cores)
Recommended: RTX 4080+ (9,728+ CUDA cores)
Optimal: RTX 5090 (16,384+ CUDA cores)
RAM: 16GB+ for large boards
KiCad: Version 7.0+

Use Cases

Primary Applications

High-density backplanes (servers, networking equipment)
GPU/CPU boards with thousands of nets
Multi-board systems with complex interconnects
Rapid prototyping where routing speed matters

Research Applications

Algorithm research - novel routing strategies
Academic projects - teaching parallel algorithms
Industry R&D - next-generation EDA tools

Community Impact

Open Source Strategy

MIT License - maximum compatibility and adoption
Modular design - easy to extend and modify
Comprehensive docs - lower barrier to contribution
Example projects - demonstrate capabilities

Industry Disruption Potential

Proof of concept for GPU-accelerated EDA tools
Performance baseline for commercial tool comparison
Research platform for next-generation routing algorithms
Democratization of advanced PCB routing capabilities

Long-term Vision

OrthoRoute 2.0 Features

Machine learning optimization of routing strategies
Multi-GPU support for massive boards
Cloud routing services for distributed teams
Advanced constraints (impedance control, crosstalk)

Ecosystem Development

Plugin marketplace for specialized routing algorithms
Integration APIs for other EDA tools (Altium, Eagle)
Educational materials for universities and training
Commercial licensing for enterprise features

Getting Started

Quick Start

```bash git clone https://github.com/username/OrthoRoute cd OrthoRoute pip install -r requirements.txt python -m orthoroute examples/simple_board/test.kicad_pcb

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