Deep Neural Network Compression with Single and Multiple Level Quantization

Understand

Network quantization is an effective solution to compress deep neural networks for practical usage.

• Existing network quantization methods cannot sufficiently exploit the depth information to generate low-bit compressed network.
• In this paper, we propose two novel network quantization approaches, single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ) for extremely low-bit quantization (ternary).We are the first to consider the network quantization from both width and depth level.
• In the width level, parameters are divided into two parts: one for quantization and the other for re-training to eliminate the quantization loss.