A technical paper titled “autoXFPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems” was published (preprint) by researchers at TU Wien, Brno University of Technology, and NYUAD.
Abstract
“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 this https URL.”
Find the technical paper here. Published March 2023.
Prabakaran, Bharath Srinivas, Vojtech Mrazek, Zdenek Vasicek, Lukas Sekanina, and Muhammad Shafique. “autoXFPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems.” arXiv preprint arXiv:2303.04734 (2023).
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