This repository contains the data and software required to reproduce the results in the paper "IIBLAST: Speeding Up Commercial FPGA Routing by Decoupling and Mitigating the Intra-CLB Bottleneck," by Shashwat Shrivastava (EPFL), Stefan Nikolic (EPFL), Chirag Ravishankar (AMD), Dinesh Gaitonde (AMD), and Mirjana Stojilovic (EPFL). The paper is accepted for publication in the Proceedings of the 42nd IEEE/ACM International Conference on Computer-Aided Design (ICCAD'23). The README file explains the structure and the contents of the repository, as well as how to use the provided software and data. Paper abstract: We identified that in modern commercial FPGAs, routing signals from the general interconnect to the configurable logic blocks (CLBs) through a very sparse input interconnect block (IIB) represents a significant runtime bottleneck. This is despite academic research usually altogether neglecting the runtime of last-mile routing through the IIB. To alleviate this bottleneck, we combine computer-aided design (CAD) and FPGA architecture enhancements. We propose a multi-stage FPGA routing approach, based on the premise that once the signals are legally routed in general interconnect—only reaching the inputs of the IIB, but not the final targets—the remaining last-mile routing through the IIB can be completed efficiently and independently for each FPGA tile. Then, the final routing solution can simply be built by joining the previously obtained partial solutions. However, we observe that some properties of modern IIB architectures limit the success rate of the intra-CLB routing, creating the need for revisiting the routing in the general interconnect and inevitably impairing the multi-stage routing runtime gains. We show that an enhanced IIB architecture mitigates the issue at a minimal cost. With ISPD16 benchmarks and an FPGA architecture model closely resembling AMD UltraScale FPGAs, we demonstrate the dominant contribution of last-mile routing to the router’s runtime. After applying our multi-stage routing approach and the proposed enhancements, we show that the observed bottleneck can be mitigated, resulting in 4.94× faster routing on average.