CRII: SHF: FSM-Centric Approximate Computing --- A Disciplined Approach

本项目提出了一种提高计算效率的新范式——以有限状态机（FSM）为中心的近似计算。近似计算在降低能耗和提高不同应用程序的性能方面显示出了希望，特别是在图像处理、机器学习和数据分析方面。目前，近似计算并不适用于FSM的计算建模，而FSM在生物科学、网络安全、数据压缩、软件工程和硬件设计等领域有着重要的应用。不断增长的数据量和计算机处理能力的限制限制了FSM？年代效率。以fsm为中心的近似计算的建立将为软件应用的效率优化打开一个新的维度的大门。本研究将利用FSM计算和近似计算之间的协同作用——FSM计算中固有的容错能力——开发一个用于探索近似FSM计算的计算平台。关键思想是对FSM可靠性的定量分析，它捕获底层近似硬件产生的错误如何通过FSM转换传播。此外，本研究还将设计和实现两种互补的近似方案——一种依赖于近似硬件的“不精确性”；另一个提供纯软件近似，并在传统的精确硬件上运行。这些近似策略将共同展示以FSM为中心的近似计算在提高FSM应用效率方面的潜力。

This project proposes a new paradigm to enhance computing efficiency --- Finite State Machine (FSM)-centric approximate computing. Approximate computing has shown promise for both reducing energy consumption and improving performance across different applications, especially those in image processing, machine learning and data analytics. To date, approximate computing has been inapplicable to FSM modeling of computations, which has important applications in domains that include biological science, cyber security, data compression, software engineering and hardware design. Growing data volumes and limitations on computer processing power constrain FSM?s efficiency. The establishment of FSM-centric approximate computing will open the door to a new dimension of efficiency optimization for software applications.This research will take advantage of the synergy between FSM computations and approximate computing --- the inherent error tolerance capability within FSM computations --- to develop a computing platform for exploring approximate FSM computations. The key idea is a quantitative analysis of FSM reliability that captures how errors generated by underlying approximate hardware propagate through FSM transitions. Additionally, this research will also design and implement two complementary approximation schemes --- one relies on the "inexactness" of approximate hardware; the other provides pure software approximation and runs on conventional exact hardware. Together these approximation strategies will demonstrate the potential of FSM-centric approximate computing in improving the efficiency of FSM applications.