Coreset-Based Neural Network Compression

Understand

We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters.

• We exploit the redundancies extant in the space of CNN weights and neuronal activations (across samples) in order to obtain compression.
• Our method requires no retraining, is easy to implement, and obtains state-of-the-art compression performance across a wide variety of CNN architectures.
• Coupled with quantization and Huffman coding, we create networks that provide AlexNet-like accuracy, with a memory footprint that is $832\times$ smaller than the original AlexNet, while also introducing significant reductions in inference time as well.