  Current state-of-the-art employs approximate multipliers to address the
highly increased power demands of DNN accelerators. However, evaluating the
accuracy of approximate DNNs is cumbersome due to the lack of adequate support
for approximate arithmetic in DNN frameworks. We address this inefficiency by
presenting AdaPT, a fast emulation framework that extends PyTorch to support
approximate inference as well as approximation-aware retraining. AdaPT can be
seamlessly deployed and is compatible with the most DNNs. We evaluate the
framework on several DNN models and application fields including CNNs, LSTMs,
and GANs for a number of approximate multipliers with distinct bitwidth values.
The results show substantial error recovery from approximate re-training and
reduced inference time up to 53.9x with respect to the baseline approximate
implementation.
