  Model compression is instrumental in optimizing deep neural network inference
on resource-constrained hardware. The prevailing methods for network
compression, namely quantization and pruning, have been shown to enhance
efficiency at the cost of performance. Determining the most effective
quantization and pruning strategies for individual layers and parameters
remains a challenging problem, often requiring computationally expensive and ad
hoc numerical optimization techniques. This paper introduces FITCompress, a
novel method integrating layer-wise mixed-precision quantization and
unstructured pruning using a unified heuristic approach. By leveraging the
Fisher Information Metric and path planning through compression space,
FITCompress optimally selects a combination of pruning mask and mixed-precision
quantization configuration for a given pre-trained model and compression
constraint. Experiments on computer vision and natural language processing
benchmarks demonstrate that our proposed approach achieves a superior
compression-performance trade-off compared to existing state-of-the-art
methods. FITCompress stands out for its principled derivation, making it
versatile across tasks and network architectures, and represents a step towards
achieving optimal compression for neural networks.
