Precision-Aware Soft Error Protection for GPUs

David J. Palframan, Nam Sung Kim, Mikko H. Lipasti
Department of Electrical and Computer Engineering, University of Wisconsin-Madison
20th IEEE International Symposium on High Performance Computer Architecture (HPCA 2014), 2014


   title={Precision-Aware Soft Error Protection for GPUs},

   author={Palframan, David J and Kim, Nam Sung and Lipasti, Mikko H},



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With the advent of general-purpose GPU computing, it is becoming increasingly desirable to protect GPUs from soft errors. For high computation throughout, GPUs must store a significant amount of state and have many execution units. The high power and area costs of full protection from soft errors make selective protection techniques attractive. Such approaches provide maximum error coverage within a fixed area or power limit, but typically treat all errors equally. We observe that for many floating-point-intensive GPGPU applications, small magnitude errors may have little effect on results, while large magnitude errors can be amplified to have a significant negative impact. We therefore propose a novel precision-aware protection approach for the GPU execution logic and register file to mitigate large magnitude errors. We also propose an architecture modification to optimize error coverage for integer computations. Our approach combines selective logic hardening, targeted checker circuits, and intelligent register file encoding for best error protection. We demonstrate that our approach can reduce the mean error magnitude by up to 87% compared to a traditional selective protection approach with the same overhead.
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