Master Thesis - Approximative Computing for CNNs

This thesis investigates how approximate arithmetic circuits impact the accuracy and hardware efficiency of convolutional neural networks for object detection and semantic segmentation. The role involves evaluating performance trade-offs, quantifying power and area savings, and exploring methods to recover lost accuracy through techniques like layer-wise multiplier selection and fine-tuning. Two Master's students will collaborate on this research using open-source libraries and simulation frameworks.