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Approximate computing is an emerging computing paradigm that offers improvedpower consumption by relaxing the requirement for full accuracy. Sincereal-world applications may have different requirements for design accuracy,one trend of approximate computing is to design runtime quality-configurablecircuits, which are able to operate under different accuracy modes withdifferent power consumption. In this paper, we present a novel framework RUCAwhich aims to approximate an arbitrary input circuit in a runtime configurablefashion. By factorizing and decomposing the truth table, our approach aims toapproximate and separate the input circuit into multiple configuration blockswhich support different accuracy levels, including a corrector circuit torestore full accuracy. By activating different blocks, the approximate circuitis able to operate at different accuracy-power configurations. To improve thescalability of our algorithm, we also provide a design space exploration schemewith circuit partitioning to navigate the search space of possibleapproximations of subcircuits during design time. We thoroughly evaluate ourmethodology on a set of benchmarks and compare against anotherquality-configurable approach, showcasing the benefits and flexibility of RUCA.For 3-level designs, RUCA saves power consumption by 36.57% within 1% error andby 51.32% within 2% error on average.

RUCA: RUntime Configurable Approximate Circuits with Self-Correcting Capability