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Abstract Ensuring the reliability of GPUs and their internal components is paramount, especially in safety-critical domains like autonomous machines and self-driving cars. These cutting-edge applications heavily rely on GPUs to implement complex algorithms due to their implicit programming flexibility and parallelism, which is crucial for efficient operation. However, as integration technologies advance, there is a growing concern regarding the potential increase in fault sensitivity of the internal components of current GPU generations. In particular, Special Function Unit (SFU) cores inside GPUs are used in multimedia, High-Performance Computing, and neural network training. Despite their frequent usage and critical role in several domains, reliability evaluations on SFUs and the development of effective mitigation solutions have yet to be studied and remain unexplored. This work evaluates the impact of transient faults in the main hardware structures of SFUs in GPUs. In addition, we analyze the main overhead costs and benefits of developing selective-hardening mechanisms for SFUs. We focus on evaluating and analyzing two SFU architectures for GPUs ( ’fused’ and ’modular’ ) and their relations to energy, area, and reliability impact on parallel applications. The experiments resort to fine-grain fault injection campaigns on an RTL GPU model (FlexGripPlus) instrumented with both SFUs. The results on both SFU architectures indicate that fused SFUs (in commercial-grade devices) require lower area overhead (about 27%) for their integration in GPUs but are more vulnerable to transient faults (in up to 47% for the analyzed cases) and less power efficient (in up to 36.6%) than modular SFUs. Moreover, the reliability estimation shows that Modular SFUs are structurally more resilient than Fused ones in up to one order of magnitude. Similarly, selective-hardening mechanism based on Triple-Modular Redundancy (TMR) shows that coarse-grain strategies might increase the reliability of the overall SFUs under feasible overhead costs.
Condia et al. (Thu,) studied this question.