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Applications in the AI and HPC fields require much memory capacity, and theamount of energy consumed by main memory of server machines is ever increasing.Energy consumption of main memory can be greatly reduced by applyingapproximate computing in exchange for increased bit error rates. AI and HPCapplications are to some extent robust to bit errors because small numericalerrors are amortized by their iterative nature. However, a single occurrence ofa NaN due to bit-flips corrupts the whole calculation result. The issue is thatfixing every bit-flip using ECC incurs too much overhead because the bit errorrate is much higher than in normal environments. We propose a low-overheadmethod to fix NaNs when approximate computing is applied to main memory. Themain idea is to reactively repair NaNs while leaving other non-fatal numericalerrors as-is to reduce the overhead. We implemented a prototype by leveragingfloating-point exceptions of x86 CPUs, and the preliminary evaluations showedthat our method incurs negligible overhead.

Reactive NaN Repair for Applying Approximate Memory to Numerical Applications