Recent efforts in open-source GPU research are opening new avenues in a domain that has long been tightly coupled with a few commercial vendors. Emerging open GPU architectures define SIMT functionality through their own ISAs, but executing existing GPU programs and optimizing performance on these ISAs relies on a compiler framework that is technically complex and often undercounted in open-hardware development costs.

Cycle-level GPU simulators are valuable educational tools, but existing frameworks are either too complex for students to navigate or too abstract to convey microarchitectural details. We present Macsim Mini, a lightweight cycle-level GPU simulator designed for computer architecture education. By concentrating on the memory hierarchy and thread scheduling rather than detailed compute pipelines, Macsim Mini captures the architectural trade-offs most central to GPU performance in a codebase small enough for students to read and modify within course assignments. Macsim Mini has been deployed in a graduate-level GPU architecture and programming course for seven semesters, serving $\sim 1,000$ students with high completion rates and average scores above 90%.

Deep neural network (DNN) inference relies heavily on Tensor Core operations, which are vulnerable to transient hardware faults in computation pipelines not protected by errorcorrecting codes (ECC). Prior fault injection work has explored both application-level and instruction-level effects on DNN accuracy. However, existing application-level approaches support only coarse perturbations and do not capture hardware execution details, while instruction-level approaches lack application-level context.To address this gap, we propose TensorDynamic, an application-aware instruction-level dynamic fault injection tool for Tensor Core execution in DNN workloads. TensorDynamic enables fine-grained fault injection into MMA (matrix-multiplyaccumulate) instructions during DNN execution. Across multiple models, we show that, under the same error injection rate and severity, application-level fault injection can produce substantially different inference outcomes from instruction-level fault injection. This result underscores the need for execution-aware fault injection when evaluating DNN resilience on GPU Tensor Cores.