  Deep Neural Networks (DNNs) are being heavily utilized in modern applications
and are putting energy-constraint devices to the test. To bypass high energy
consumption issues, approximate computing has been employed in DNN accelerators
to balance out the accuracy-energy reduction trade-off. However, the
approximation-induced accuracy loss can be very high and drastically degrade
the performance of the DNN. Therefore, there is a need for a fine-grain
mechanism that would assign specific DNN operations to approximation in order
to maintain acceptable DNN accuracy, while also achieving low energy
consumption. In this paper, we present an automated framework for
weight-to-approximation mapping enabling formal property exploration for
approximate DNN accelerators. At the MAC unit level, our experimental
evaluation surpassed already energy-efficient mappings by more than $\times2$
in terms of energy gains, while also supporting significantly more fine-grain
control over the introduced approximation.
