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：近似电路与加速器的生成及探索一直是能效和/或性能提升领域的重要研究方向。由于ASIC与FPGA架构的固有差异，现有研究主要聚焦于ASIC，在部署至FPGA加速器系统时未能实现同等增益。本文提出autoXFPGAs框架，通过利用统计或机器学习模型，基于目标应用的简单RTL描述，有效探索当前最先进ASIC近似电路的架构空间，使其适配于FPGA系统。该完整框架为开源项目，可通过https://github.com/ehw-fit/autoxfpgas在线获取。