← 研踪 PaperTrailarXiv:2610.33510 · v2
DiffRouter: A Differentiable Routing Framework for UltraScale FPGAs
首次提交 2026-10-24 15:35
版本更新 2026-10-27 15:59
雷达观测 2026-10-29 00:35
摘要
Routing has become a major bottleneck in large-scale FPGA design flows, where traditional routers rely on sequential net-by-net processing and rip-up-reroute heuristics. This inherent sequentialism limits parallelism, leads to suboptimal utilization of routing resources, and significantly increases runtime on modern FPGAs. This paper introduces DiffRouter, the first differentiable, gradient-based FPGA routing framework with GPU acceleration. By lifting routing into a continuous optimization space, DiffRouter enables highly concurrent routing and improved global optimality before projecting solutions back onto the discrete fabric. The framework consists of three key stages: (1) Resource-pruned preprocessing, which extracts compact routing variables per net to reduce memory footprint and problem complexity; (2) Differentiable global routing via gradient descent, which formulates routing as a Lagrangian-relaxed optimization that minimizes wirelength while iteratively enforcing connectivity and congestion constraints as relaxed terms; and (3) Postprocessing, which maps the continuous wire distributions to a valid UltraScale routing solution and completes solutions by detailed routing. Experiments on the FPGA 2024 Routing Contest benchmarks show that DiffRouter outperforms state-of-the-art parallel routers, achieving over 5x speedup over RWRoute and 2x over Vivado, and running 18% faster than the state-of-the-art academic FPGA router Potter while delivering competitive critical path wirelength.
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v2 · 2026-10-27 15:59 · 最新观测版本
DiffRouter: A Differentiable Routing Framework for UltraScale FPGAs
Routing has become a major bottleneck in large-scale FPGA design flows, where traditional routers rely on sequential net-by-net processing and rip-up-reroute heuristics. This inherent sequentialism limits parallelism, leads to suboptimal utilization of routing resources, and significantly increases runtime on modern FPGAs. This paper introduces DiffRouter, the first differentiable, gradient-based FPGA routing framework with GPU acceleration. By lifting routing into a continuous optimization space, DiffRouter enables highly concurrent routing and improved global optimality before projecting solutions back onto the discrete fabric. The framework consists of three key stages: (1) Resource-pruned preprocessing, which extracts compact routing variables per net to reduce memory footprint and problem complexity; (2) Differentiable global routing via gradient descent, which formulates routing as a Lagrangian-relaxed optimization that minimizes wirelength while iteratively enforcing connectivity and congestion constraints as relaxed terms; and (3) Postprocessing, which maps the continuous wire distributions to a valid UltraScale routing solution and completes solutions by detailed routing. Experiments on the FPGA 2024 Routing Contest benchmarks show that DiffRouter outperforms state-of-the-art parallel routers, achieving over 5x speedup over RWRoute and 2x over Vivado, and running 18% faster than the state-of-the-art academic FPGA router Potter while delivering competitive critical path wirelength.
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