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This paper introduces Microarchitecture Cliffs, a benchmark generation methodology to identify and attribute microarchitectural mismatches between architectural simulators and RTL implementations for model calibration. The Cliff methodology generates benchmarks that isolate individual microarchitectural features, enabling precise attribution of behavioral differences. Applying this methodology to calibrate XS-GEM5 against XS-RTL, the authors reduced performance error on Cliff benchmarks from 59.2% to 1.4% and improved performance prediction accuracy on SPEC2017 benchmarks.
Pinpointing mismatches between architectural simulators and RTL implementations is now far easier, thanks to a new benchmark generation methodology that isolates single microarchitectural features.
Architectural simulators play a critical role in early microarchitectural exploration due to their flexibility and high productivity. However, their effectiveness is often constrained by fidelity: simulators may deviate from the behavior of the final RTL, leading to unreliable performance estimates. Consequently, model calibration, which aligns simulator behavior with the RTL as the ground-truth microarchitecture, becomes essential for achieving accurate performance modeling. To facilitate model calibration accuracy, we propose Microarchitecture Cliffs, a benchmark generation methodology designed to expose mismatches in microarchitectural behavior between the simulator and RTL. After identifying the key architectural components that require calibration, the Cliff methodology enables precise attribution of microarchitectural differences to a single microarchitectural feature through a set of benchmarks. In addition, we develop a set of automated tools to improve the efficiency of the Cliff workflow. We apply the Cliff methodology to calibrate the XiangShan version of gem5 (XS-GEM5) against the XiangShan open-source CPU (XS-RTL). We reduce the performance error of XS-GEM5 from 59.2% to just 1.4% on the Cliff benchmarks. Meanwhile, the calibration guided by Cliffs effectively reduces the relative error of a representative tightly coupled microarchitectural feature by 48.03%. It also substantially lowers the absolute performance error, with reductions of 15.1% and 21.0% on SPECint2017 and SPECfp2017, respectively.
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