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


利用快速增长的大量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，通过牺牲精度来换取节能来提高计算系统的效率。然而，对于计算精度的要求并不固定。在运行时动态控制所应用的近似水平是有效优化能量的关键，同时仍然包含和限制运行时引起的误差。在本文中，我们提出并实现了一种自动且独立于电路的设计框架，该框架可在运行时生成具有动态可重新配置精度的近似电路。生成的电路具有不同的精度级别，也支持准确的执行。使用行业实力流程和电路进行的广泛实验评估表明，我们生成的近似电路在 2% 误差范围内将能量提高了高达 41%，在假设完全精度要求为 33% 的悲观场景下平均提高了 17.5%运行时。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，即 7nm 传统 FinFET 和以增加动态功耗的高成本提高性能的新兴技术。