gem5 + rtl: A framework to enable RTL models inside a full-system simulator

In recent years there has been a surge of interest in designing custom accelerators for power-efficient high-performance computing. However, available tools to simulate low-level RTL designs often neglect the target system in which the design will operate. This hinders proper testing and debugging of functionalities, and does not allow co-designing the accelerator to obtain a balanced and efficient architecture. In this paper, we introduce gem5 + rtl, a flexible framework that enables simulation of RTL models inside a full-system software simulator. We present the framework’s functionality that allows easy integration of RTL models on a simulated system-on-chip (SoC) that is able to boot Linux and run complex multi-threaded and multi-programmed workloads. We demonstrate the framework with two relevant use cases that integrate a multi-core SoC with a Performance Monitoring Unit (PMU) and the NVIDIA Deep Learning Accelerator (NVDLA), showcasing how the framework enables testing RTL model features and how it can enable co-design taking into account the entire SoC.This research was supported by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014-2020 with a grant of 50% of total cost eligible under the DRAC project [001-P- 001723], by the Spanish goverment (grant RTI2018-095094- B-C21 CONSENT), by the Spanish Ministry of Science and Innovation (contracts PID2019-107255GB-C21) and by the Catalan Government (contracts 2017-SGR-1414, 2017-SGR705). This work has also been supported by the European Community’s Horizon 2020 Framework Programme under the Mont-Blanc 2020 and EPI projects (grant agreements n. 779877 and n. 826647); and by the Arm-BSC Center of Excellence. G. López-Paradís has been partially supported by the Agency for Management of University and Research Grants (AGAUR) of the Government of Catalonia under Ajuts per a la contractació de personal investigador novell fellowship No. 2021FI B00994. A. Armejach has been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under Juan de la Cierva postdoctoral fellowship number IJCI-2017-33945. M. Moretó has been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under Ramón y Cajal fellowship No. RYC-2016-21104.Peer ReviewedPostprint (author's final draft