Extreme Model Compression with Structured Sparsity at Low Precision

The introduction of the SLOPE framework marks a significant advancement in the field of deep learning, particularly for deploying deep neural networks (DNNs) on devices with limited resources. Traditional methods like weight quantization and structured sparsity have been effective individually but often lead to performance degradation when combined. SLOPE addresses this issue by promoting angular alignment between full-precision weights and their sparse, quantized counterparts, thus minimizing discrepancies. The framework has been tested on models such as ResNet-18, achieving a remarkable 20-fold reduction in model size while maintaining approximately 99% accuracy. This breakthrough not only enhances the efficiency of DNNs but also opens new avenues for their application in various fields, making advanced AI technologies more accessible and practical for real-world use.