Daniel J. Sorin

Ralph Nathan, Helia Naeimi, Daniel J. Sorin et al.

We present a scheme to automatically set the precision of floating point variables in an application. We design a framework that profiles applications to measure undesirable numerical behavior at the floating point operation level. We use this framework to perform mixed precision analysis to heuristically set the precision of all variables in an application based on their numerical profiles. We experimentally evaluate the mixed precision analysis to show that it can generate a range of results with different accuracy and performance characteristics.

Yaqi Zhang, Ralph Nathan, Daniel J. Sorin

In this paper, we use reduced precision checking (RPC) to detect errors in floating point arithmetic. Prior work explored RPC for addition and multiplication. In this work, we extend RPC to a complete floating point unit (FPU), including division and square root, and we present precise analyses of the errors undetectable with RPC that show bounds that are smaller than prior work. We implement RPC for a complete FPU in RTL and experimentally evaluate its error coverage and cost.