The rapid end of Moore’s Law and Dennard’s Scaling has driven computing systems, from smartphones to supercomputers, to embrace heterogeneous architectures for continued efficiency gains. As specialized, compute-intensive workloads such as machine learning become increasingly prominent, there is a critical need for accurate, open-source simulation tools to model and evaluate the next generation of hardware accelerators. However, the pace of innovation in accelerator architectures, particularly graphics processing units (GPUs), has outstripped the capabilities of existing public simulation frameworks, limiting the research community’s ability to explore new ideas and validate results. This project addresses these challenges by enhancing the widely used Accel-Sim simulation infrastructure, enabling detailed, validated modeling of modern and future accelerators. The proposed enhancements will empower a broad community of researchers to advance innovations in computer architecture, improve system efficiency, and support the development of emerging applications that rely on high-performance accelerators. This award will significantly extend Accel-Sim’s capabilities through three major technical thrusts. First, the project will modernize and expand Accel-Sim’s performance and energy models to support the latest GPU architectures (including NVIDIA’s Ampere, Hopper, and Blackwell), incorporating features such as transformer engines, sparse tensor cores, and support for asynchron