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Generation and exploration of approximate circuits and accelerators has beena prominent research domain to achieve energy-efficiency and/or performanceimprovements. This research has predominantly focused on ASICs, while notachieving similar gains when deployed for FPGA-based accelerator systems, dueto the inherent architectural differences between the two. In this work, wepropose a novel framework, Xel-FPGAs, which leverages statistical or machinelearning models to effectively explore the architecture-space ofstate-of-the-art ASIC-based approximate circuits to cater them for FPGA-basedsystems given a simple RTL description of the target application. We have alsoevaluated the scalability of our framework on a multi-stage application using ahierarchical search strategy. The Xel-FPGAs framework is capable of reducingthe exploration time by up to 95%, when compared to the default synthesis,place, and route approaches, while identifying an improved set ofPareto-optimal designs for a given application, when compared to thestate-of-the-art. The complete framework is open-source and available online athttps://github.com/ehw-fit/xel-fpgas.

Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems