The approximate computing paradigm advocates for relaxing accuracy goals in applications to improve energy-efficiency and performance. Recently, this paradigm has been explored to improve the energy-efficiency of silicon photonic networks-on-chip (PNoCs). Silicon photonic interconnects suffer from high power dissipation because of laser sources, which generate carrier wavelengths, and tuning power required for regulating photonic devices under different uncertainties. In this article, we propose a framework called AppRoXimation framework for On-chip photonic Networks ( ARXON ) to reduce such power dissipation overhead by enabling intelligent and aggressive approximation during communication over silicon photonic links in PNoCs. Our framework reduces laser and tuning-power overhead while intelligently approximating communication, such that application output quality is not distorted beyond an acceptable limit. Simulation results show that our framework can achieve up to 56.4% lower laser power consumption and up to 23.8% better energy-efficiency than the best-known prior work on approximate communication with silicon photonic interconnects and for the same application output quality.

中文翻译：

---

阿森: 一种通过片上光子网络进行近似通信的框架

近似计算范式提倡放宽应用程序中的准确度目标，以提高能效和性能。最近，已经探索了这种范式以提高片上硅光子网络 (PNoC) 的能效。硅光子互连由于产生载流子波长的激光源以及在不同不确定性下调节光子器件所需的调谐功率而遭受高功率耗散。在本文中，我们为片上光子网络提出了一个称为 AppRoXimation 框架的框架（ 阿森 ) 通过在 PNoC 中的硅光子链路通信期间启用智能和积极的近似来减少这种功耗开销。我们的框架减少了激光和调谐功率开销，同时智能地接近通信，这样应用程序输出质量不会超过可接受的限制。仿真结果表明，在相同应用输出质量的情况下，我们的框架可以将激光功耗降低多达 56.4%，并将能源效率提高多达 23.8%。