论文标题
RUCA：具有自我校正功能的运行时可配置的近似电路
RUCA: RUntime Configurable Approximate Circuits with Self-Correcting Capability
论文作者
论文摘要
近似计算是一种新兴的计算范式，它通过放松准确性的要求来提供改进的功耗。由于现实世界的应用程序可能对设计准确性有不同的要求，因此近似计算的一个趋势是设计运行时质量可配置的电路，这些电路能够在不同的精度模式下以不同的功耗。在本文中，我们提出了一个新颖的框架RUCA，旨在以可配置的方式近似任意输入电路。通过分解和分解真实表，我们的方法旨在将输入电路分为多个配置块，这些配置块支持不同的精度级别，包括校正电路以恢复完整的精度。通过激活不同的块，近似电路能够以不同的精度功率配置进行操作。为了提高我们的算法的可扩展性，我们还提供了一个设计空间探索方案，并使用电路分配，以在设计时间期间导航子电路可能近似值的搜索空间。我们对一组基准进行了彻底评估我们的方法，并与另一种可质量配置的方法进行比较，展示了RUCA的益处和灵活性。对于3级设计，RUCA在误差1％的误差范围内将功率消耗量下降了36.57％，平均误差2％的误差率为51.32％。
Approximate computing is an emerging computing paradigm that offers improved power consumption by relaxing the requirement for full accuracy. Since real-world applications may have different requirements for design accuracy, one trend of approximate computing is to design runtime quality-configurable circuits, which are able to operate under different accuracy modes with different power consumption. In this paper, we present a novel framework RUCA which aims to approximate an arbitrary input circuit in a runtime configurable fashion. By factorizing and decomposing the truth table, our approach aims to approximate and separate the input circuit into multiple configuration blocks which support different accuracy levels, including a corrector circuit to restore full accuracy. By activating different blocks, the approximate circuit is able to operate at different accuracy-power configurations. To improve the scalability of our algorithm, we also provide a design space exploration scheme with circuit partitioning to navigate the search space of possible approximations of subcircuits during design time. We thoroughly evaluate our methodology on a set of benchmarks and compare against another quality-configurable approach, showcasing the benefits and flexibility of RUCA. For 3-level designs, RUCA saves power consumption by 36.57% within 1% error and by 51.32% within 2% error on average.
