Towards Accurate RISC-V Full System Simulation via Component-Level Calibration
Karan Pathak (École Polytechnique Fédérale de Lausanne, Student TU Delft, Haute Ecl. d'Ingenierie et de Gestion du Canton de Vaud Inst. Reconfigurable Embedded Digit. Syst.)
Joshua Klein (École Polytechnique Fédérale de Lausanne)
Giovanni Ansaloni (École Polytechnique Fédérale de Lausanne)
Said Hamdioui (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Georgi Gaydadjiev (TU Delft - Electrical Engineering, Mathematics and Computer Science, Chalmers University of Technology)
Marina Zapater (Haute Ecole d'Ingenierie et de Gestion du Canton de Vaud, École Polytechnique Fédérale de Lausanne)
David Atienza (École Polytechnique Fédérale de Lausanne)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Full-System (FS) simulation is essential for performance evaluation of complete systems that execute complex applications on a complete software stack consisting of an operating system and user applications. Nevertheless, they require careful fine-tuning against real hardware to obtain reliable performance statistics, which can become tedious, error-prone, and time-consuming with typical trial-and-error approaches. We propose a novel, streamlined, component-level calibration methodology to address these shortcomings to validate FS simulation models. Our methodology greatly accelerates the validation process without sacrificing accuracy. It is Instruction Set Architecture (ISA)-agnostic, and can tackle hardware specifications at different levels of detail. We demonstrate its effectiveness by validating FS models against both open-hardware and IP-protected (closed hardware) RISC-V silicon, achieving a mean error of 19%-23% for the SPEC CPU2017 suite in the two cases. We introduce the first open-source RISC-V-based FS-validated simulation models with a complete and replicable methodology.