Many algorithms feature an iterative loop that converges to the result of interest. The numerical operations in such algorithms are generally implemented using finite-precision arithmetic, either fixed or floating point, most of which operate least-significant digit first. This results in a fundamental problem: if, after some time, the result has not converged, is this because we have not run the algorithm for enough iterations or because the arithmetic in some iterations was insufficiently precise? There is no easy way to answer this question, so users will often over-budget precision in the hope that the answer will always be to run for a few more iterations. We propose a fundamentally new approach: armed with the appropriate arithmetic able to generate results from most-significant digit first, we show that fixed compute-area hardware can be used to calculate an arbitrary number of algorithmic iterations to arbitrary precision, with both precision and iteration index increasing in lockstep. Thus, datapaths constructed following our principles demonstrate efficiency over their traditional arithmetic equivalents where the latter’s precisions are either under- or over-budgeted for the computation of a result to a particular accuracy. For the execution of 100 iterations of the Jacobi method, we obtain a 1.60x increase in frequency and 15.7x LUT and 50.2x flip-flop reductions over a 2048-bit parallel-in, serial-out traditional arithmetic equivalent, along with 46.2x LUT and 83.3x flip-flop decreases versus the state-of-the-art online arithmetic implementation.