The demand for Deep Learning applications in resource constrained devices is booming in recent years. The
use of Deep Neural Network (DNN) is the leading method in these applications which has error resilient nature.
This allows the use of Approximate Computing for efficient computation to leverage efficiency accuracy trade
off by replacing Approximate Multiplier in place of exact multipliers. In this paper we proposed Approximate
Compressors and compared them in different cases of 8 bit integer dadda multipliers in terms of the Error metrics
and accuracy in real life object classification applications. The approximate multipliers are designed using different
compressors and used to perform multiplication in ResNet. We have proposed two approximate compressors
designs Design 1 and Design 2.The proposed 4:2 compressors design shows the more correct outputs and less
Worst Case Relative Error (WCRE) in the range of 2-16. Our proposed 4:2 compressor Design1 is utilized in the
modified Reduction circuitry of dadda multiplier and shows the accuracy of 81.6 % for DNN application
This work is licensed under a Creative Commons Attribution 4.0 International License.
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