Fighting Quantization Bias With Bias

Understand

Low-precision representation of deep neural networks (DNNs) is critical for efficient deployment of deep learning application on embedded platforms, however, converting the network to low precision degrades its performance.

• Crucially, networks that are designed for embedded applications usually suffer from increased degradation since they have less redundancy.
• This is most evident for the ubiquitous MobileNet architecture which requires a costly quantization-aware training cycle to achieve acceptable performance when quantized to 8-bits.
• In this paper, we trace the source of the degradation in MobileNets to a shift in the mean activation value.