Variability-Aware Approximate Circuit Synthesis via Genetic Optimization
<|reference_start|>Variability-Aware Approximate Circuit Synthesis via Genetic Optimization: One of the major barriers that CMOS devices face at nanometer scale is increasing parameter variation due to manufacturing imperfections. Process variations severely inhibit the reliable operation of circuits, as the operational frequency at the nominal process corner is insufficient to suppress timing violations across the entire variability spectrum. To avoid variability-induced timing errors, previous efforts impose pessimistic and performance-degrading timing guardbands atop the operating frequency. In this work, we employ approximate computing principles and propose a circuit-agnostic automated framework for generating variability-aware approximate circuits that eliminate process-induced timing guardbands. Variability effects are accurately portrayed with the creation of variation-aware standard cell libraries, fully compatible with standard EDA tools. The underlying transistors are fully calibrated against industrial measurements from Intel 14nm FinFET in which both electrical characteristics of transistors and variability effects are accurately captured. In this work, we explore the design space of approximate variability-aware designs to automatically generate circuits of reduced variability and increased performance without the need for timing guardbands. Experimental results show that by introducing negligible functional error of merely $5.3\times 10^{-3}$, our variability-aware approximate circuits can be reliably operated under process variations without sacrificing the application performance.<|reference_end|>
@article{balaskas2023variability-aware,
    title={Variability-Aware Approximate Circuit Synthesis via Genetic Optimization},
    author={Konstantinos Balaskas, Florian Klemme, Georgios Zervakis, Kostas
  Siozios, Hussam Amrouch, J"org Henkel},
    journal={Volume number: 69; Year: 2022; Pages: 4141-4153},
    year={2023},
    doi={10.1109/TCSI.2022.3183858},
    archivePrefix={arXiv},
    eprint={2404.04258},
    primaryClass={cs.AR cs.NE}
}