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We have developed and evaluated Argus-G, an error detection scheme for general purpose GPU (GPGPU) cores. Argus-G is a natural extension of the Argus error detection scheme for CPU cores, and we demonstrate how to modify Argus such that it is compatible with GPGPU cores. Using an RTL prototype, we experimentally show that Argus-G can detect the vast majority of injected errors at relatively low performance,...
In this work, we provide energy-efficient architectural support for floating point accuracy. For each floating point addition performed, we "recycle" that operation's rounding error. We make this error architecturally visible such that it can be used, whenever desired, by software. We also design a compiler pass that allows software to automatically use this feature. Experimental results...
We propose a new, low-cost, hardware-only scheme to detect errors in superscalar, out-of-order processor cores. For each instruction decoded, Nostradamus compares what the instruction is expected to do against what the instruction actually does. We implement Nostradamus in RTL on top of a baseline superscalar, out-of-order core, and we experimentally evaluate its ability to detect injected errors...
Prior work developed an efficient technique, called reduced precision checking, for detecting errors in floating point addition. In this work, we extend reduced precision checking (RPC) to multiplication. Our results show that RPC can successfully detect errors in floating point multiplication at relatively low cost.
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