Approximate Multiplier Mapping for Unfairness Mitigation in Energy-Efficient DNNs

Embedded devices struggle with the heavy computational demands of extensive neural network models, a problem partially addressed by integrating accelerators with numerous multiply-accumulate units. However, this solution increases energy consumption. While using approximate circuits in accelerators can lower energy usage, it compromises accuracy and raises concerns about maintaining fair inference across diverse populations, particularly in the medical field. This work leverages approximate multipliers in deep neural networks to retain fairness and reduce energy consumption while keeping the inference accuracy within strict thresholds.