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The breakdown in Moore’s Law and Dennard Scaling is leading to drastic changes in the makeup and constitution of computing systems. For example, a single chip integrates 10-100s of cores and has a heterogeneous mix of general-purpose compute engines and highly specialized accelerators. Traditionally, computer architects have relied on tools like architectural simulators (e.g., Accel-Sim, gem5, gem5-SALAM, GPGPU-Sim, MGPUSim, Sniper-Sim, and ZSim) to accurately perform early stage prototyping and optimizations for the proposed research. However, as systems become increasingly complex and heterogeneous, architectural tools are straining to keep up. In particular, publicly available architectural simulators are often not very representative of the industry parts they intend to represent. This leads to a mismatch in expectations; when prototyping new optimizations in gem5 users may draw the wrong conclusions about the efficacy of proposed optimizations if the tool’s models do not provide high fidelity. In this work, we focus on the gem5 simulator, the most popular platform for computer system simulation. In recent years gem5 has been used by ∼20% of simulation-based papers published in top-tier computer architecture conferences per year. Moreover, gem5 can run entire systems, including CPUs, GPUs, and accelerators as well as the operating system, runtime, network and other related components (including multiple ISAs). Thus, gem5 has the potential to allow users to study the behavior of the entire heterogeneous systems. Unfortunately, some of gem5’s models do not always provide high accuracy relative to their ”real” counterparts. In particular, although gem5’s GPU model provides high accuracy internally at AMD [9], the publicly available gem5 GPU model is often inaccurate, especially for the memory subsystem. To understand this, we designed a series of microbenchmarks designed to expose the latencies, bandwidths, and sizes of a variety of GPU components on real AMD GPUs. Our results showed that while gem5’s GPU microarchitecture was relatively accurate (within 5-10% in most cases), gem5’s memory subsytem was off by an average of 272% (645% max) for latency and 70% (693% max) for bandwidth. Accordingly, to help bridge this divide, we propose to design and use a new tool, GPU Accuracy Profiler (GAP), to compare and improve the behavior of gem5’s simulated GPUs relative to real GPUs. By iteratively applying fixes and improvements to gem’s GPU model via GAP, we will significantly improved its fidelity relative to real AMD GPUs. Although this work is still ongoing, our preliminary results show significant promise: on average 25% error for latency and 16% error for bandwidth, respectively. Overall, by completing this work we hope to enable more widespread adoption of gem5 as an accurate platform for heterogeneous architecture research.