Leveraging the inherent error tolerance of a vast number of application domains that are rapidly growing, approximate computing arises as a design alternative to improve the efficiency of our computing systems by trading accuracy for energy savings. However, the requirement for computational accuracy is not fixed. Controlling the applied level of approximation dynamically at runtime is a key to effectively optimize energy, while still containing and bounding the induced errors at runtime. In this paper, we propose and implement an automatic and circuit independent design framework that generates approximate circuits with dynamically reconfigurable accuracy at runtime. The generated circuits feature varying accuracy levels, supporting also accurate execution. Extensive experimental evaluation, using industry strength flow and circuits, demonstrates that our generated approximate circuits improve the energy by up to 41% for 2% error bound and by 17.5% on average under a pessimistic scenario that assumes full accuracy requirement in the 33% of the runtime. To demonstrate further the efficiency of our framework, we considered two state-of-the-art technology libraries which are a 7nm conventional FinFET and an emerging technology that boosts performance at a high cost of increased dynamic power.

中文翻译：

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具有运行时可重构精度的近似电路的设计自动化

利用大量快速增长的应用领域固有的容错能力，近似计算作为一种设计替代方案出现，通过以节省能源的准确性来提高我们的计算系统的效率。但是，对计算精度的要求不是固定的。在运行时动态控制应用的近似水平是有效优化能量的关键，同时在运行时仍然包含和限制引起的误差。在本文中，我们提出并实现了一个自动和电路独立的设计框架，该框架在运行时生成具有动态可重构精度的近似电路。生成的电路具有不同的精度级别，也支持准确的执行。广泛的实验评估，使用行业实力流程和电路，证明我们生成的近似电路在 2% 的误差范围内将能量提高了 41%，在假设运行时间的 33% 的完全精度要求的悲观场景下平均提高了 17.5%。为了进一步证明我们框架的效率，我们考虑了两个最先进的技术库，它们是 7nm 传统 FinFET 和新兴技术，以增加动态功率的高成本提高性能。