abstract
- Deep learning models are increasingly deployed in performance-critical applications, where computational efficiency must be balanced with accuracy. Model compression techniques such as pruning and quantization help address this challenge. However, they are often evaluated solely on accuracy, overlooking their impact on model size and inference time for a given hardware setup.