ConfAx: Exploiting Approximate Computing for Configurable FPGA CNN Acceleration at the Edge

The number of CNN-based applications executing at the Edge has been considerably increasing. Considering that CNNs are recognized error-resilient and the varied Edge conditions, we exploit hardware-level Approximate Computing to optimize FPGA-based CNN accelerators without any model retraining or other modifications. Given that, we propose ConfAx, a fully configurable multi-target Framework that navigates the accuracy-performance-resource trade-off to deploy different versions of CNN FPGA approximate accelerators. With an Edge case study (video surveillance), we show that ConfAx reduces power (up to $1.65\times)$ and energy (up to $ 1.44\times$) over a state-of-the-art accelerator at minor accuracy penalties (0.88% on average).