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In this work, we evaluate aggressive undervolting, i.e., voltage underscaling below the nominal level to reduce the energy consumption of Field Programmable Gate Arrays (FPGAs). Usually, voltage guardbands are added by chip vendors to ensure the worst-case process and environmental scenarios. Through experimenting on several FPGA architectures, we con¿rm a large voltage guardband for several FPGA components, which in turn, delivers signi¿cant power savings. However, further undervolting below the voltage guardband may cause reliability issues as the result of the circuit delay increase, and faults might start to appear. We extensively characterize the behavior of these faults in terms of the rate, location, type, as well as sensitivity to environmental temperature, primarily focusing on FPGA on-chip memories, or Block RAMs (BRAMs). Understanding this behavior can allow to deploy ef¿cient mitigation techniques, and in turn, FPGA-based designs can be improved for better energy, reliability, and performance trade-offs. Finally, as a case study, we evaluate a typical FPGA-based Neural Network (NN) accelerator when the FPGA voltage is underscaled. In consequence, the substantial NN energy savings come with the cost of NN accuracy loss. To attain power savings without NN accuracy loss below the voltage guardband gap, we proposed an application-aware technique and we also, evaluated the built-in Error-Correcting Code (ECC) mechanism. Hence, First, we developed an application-dependent BRAMs placement technique that relies on the deterministic behavior of undervolting faults, and mitigates these faults by mapping the most reliability sensitive NN parameters to BRAM blocks that are relatively more resistant to undervolting faults. Second, as a more general technique, we applied the built-in ECC of BRAMs and observed a signi¿cant fault coverage capability thanks to the behavior of undervolting faults, with a negligible power consumption overhead.