Simulating Inexact DNNs for Use in Custom Hardware Architectures

In response to the growing complexity of Deep Neural Network (DNN) models, the paradigm of approximate computing has emerged as a compelling approach to strike a balance between computational efficiency and model accuracy. Approximate computing involves intentionally allowing errors using inexact computations, exploiting the inherent resilience of many applications to such inaccuracies. While current state-of-the-art approaches leverage approximate multipliers for approximate DNN accelerators, there has been a notable difficulty in emulating such DNNs on common frameworks such as PyTorch, as they do not inherently support inexact arithmetic. This paper introduces a seamless PyTorch plugin, to evaluate inexact DNN models while having GPU acceleration to enable rapid emulation. Additionally, we support approximate-aware retraining and perform evaluation on several popular convolutional neural networks on Cifar10 and ImageNet datasets.