8 Neural Network Compression Techniques For ML Developers

In addition, recent years witnessed significant progress in virtual reality, augmented reality, and smart wearable devices, creating challenges in deploying deep learning systems to portable devices with limited resources (e.g. Now let's take a look at a few papers that introduced novel compression models: In this paper, the authors propose two novel network quantization approaches single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ). The network quantization is considered from both width and depth level. In this paper the authors proposed an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, this method considers the whole network to choose the right rank configuration. It combines three techniques -- value quantization with sparsity multiplication, base encoding, and zero-run encoding.