Generation and exploration of approximate circuits and accelerators has been a prominent research domain exploring energy-efficiency and/or performance improvements. This research has predominantly focused on ASICs, while not achieving similar gains when deployed for FPGA-based accelerator systems, due to the inherent architectural differences between the two. In this work, we propose the autoXFPGAs framework, which leverages statistical or machine learning models to effectively explore the architecture-space of state-of-the-art ASIC-based approximate circuits to cater them for FPGA-based systems given a simple RTL description of the target application. The complete framework is open-source and available online at https://github.com/ehw-fit/autoxfpgas.
翻译:近似电路和加速器的生成和探索一直是探索能源效率和/或性能改进的一个突出的研究领域,这项研究主要侧重于ASIC,但由于两者之间固有的建筑差异,在为基于FPGA的加速器系统部署时没有取得类似成果,但由于两者之间固有的建筑差异,因此没有取得类似成果。我们提议采用自动XFPGAs框架,利用统计或机器学习模型来有效探索基于先进ACIC的近似电路的结构-空间,以便根据对目标应用的简单RTL说明,为基于FPGA的系统服务。完整的框架是开放的,可在https://github.com/ehw-fit/autooxfpgas上查阅。</s>