Accuracy, Cost, and Performance Tradeoffs for Floating-Point Accumulation

Set-wise floating point accumulation is a fundamental operation in scientific computing, but it presents design challenges such as data hazard between the output and input of the deeply pipelined floating point adder and numerical accuracy of results. Streaming reduction architectures on FPGAs generally do not consider the floating point error, which can become a significant factor due to the dynamic nature of reduction architectures and the inherent roundoff error and non-associativity of floating-point addition. In this paper we two frameworks using our existing reduction circuit architecture based on compensated summation for improving accuracy of results. We find that both these implementations provide almost 50% exact results for most of the datasets and relative error is less than that for the reduction circuit. These designs require more than twice the resources and operate at less frequency when compared to the original reduction circuit.