Floating-point sums and dot products accumulate rounding errors that may render the result very inaccurate. To address this, Kulisch proposed to use an internal accumulator large enough to cover the full exponent range of floating-point. With it, sums and dot products become exact operations. This idea failed to materialize in general purpose processors, as it was considered to slow and/or too expensive in terms of resources. It may however be an interesting option in recon-figurable computing, where a designer may use use smaller, more resource-efficient floating-point formats, knowing that sums and dot products will be exact. Another motivation of this work is that these exact operations, contrary to classical floating point ones, are associative, which enables better compiler optimizations. This work therefore compares, in the context of modern FPGAs, several implementations of the Kulisch accumulator: three proposed by Kulisch, and two novel ones. These architectures are implemented in a VivadoHLS-compliant C++ generator that is fully customiz-able. Comparisons targeting Xilinx's Kintex 7 FPGAs show improvement over Kulisch' proposal in both area and speed. In single precision, compared with a naive use of classical operators , the proposed accumulator runs at similar frequency, consumes 10x more resource in single precision, but reduces the overall latency of a large dot product by 25x while vastly improving accuracy.