Martino, GianlucaGianlucaMartinoGarcia-Ortiz, AlbertoAlbertoGarcia-OrtizSchammer, LutzLutzSchammerFey, GörschwinGörschwinFey2025-12-152025-12-152025-10IEEE Nordic Circuits and Systems Conference, NorCAS 2025https://hdl.handle.net/11420/60241Applications such as image processing and machine learning can tolerate computational inaccuracies, allowing for the use of approximate circuits that trade precision for reduced power consumption and improved performance. Traditional testing methods, designed for exact computing, often discard a circuit upon detecting any defect, which is too strict for approximate circuits where minor deviations are acceptable. To overcome this limitation, we introduce and formalize a novel testing methodology for approximate circuits that quantifies the impact of defects on overall performance. Our method assesses the degree of approximation by measuring how defects affect computational accuracy. Instead of a binary pass/fail outcome, this technique grades the quality of the circuit, enabling a precise test-based separation of acceptable and unacceptable circuits. This approach not only enhances testing efficiency but also expands the potential for deploying approximate circuits across various fields.entestingapproximate computingfault toleranceComputer Science, Information and General Works::004: Computer SciencesConference Paper10.1109/norcas66540.2025.11231275Conference Paper