Evaluating GPU's Instruction-Level Error Characteristics Under Low Supply Voltages

Jingweijia Tan, Jiashuo Wang, Kaige Yan, Xiaohui Wei, Xin Fu

Abstract: Supply voltage underscaling has been an effective approach to improve the energy-efficiency of modern high-performance processors, such as GPUs. However, energy efficiency and reliability are two sides of a trade-off. Undervolting will inevitably undermine reliability, since it reduces chip manufacturers’ voltage guardbands that is designed to ensure correct operations under worst-case scenarios. To achieve optimal energy efficiency while maintaining enough reliability, it is necessary to deeply understand the error characteristics caused by undervolting. Unlike previous works which focus mostly on program level, we perform the first comprehensive instruction-level voltage margin and error characteristics evaluation for GPU architectures. We systematically measure the error probability and patterns of GPU instructions during undervolting. Then, we also analyze the impact of locations (SMs, threads, and bits) and operand data values on the error characteristics. Based on our observations, we reduce the voltage to the minimum safe limit for different instructions which achieves 18.37% energy saving, and we further propose an error detection strategy which reduces the performance and energy overhead by 14.8% with negligible 0.01% degradation for error detection rate.