Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
This work addresses the accuracy loss in low-bit DNNs for embedded systems, representing an incremental improvement over existing quantization methods.
The paper tackles the problem of accuracy drop in low-bit deep neural networks (DNNs) for embedded applications by proposing a stochastic quantization (SQ) algorithm, which gradually quantizes elements/filters based on error probability, resulting in consistent and significant accuracy improvements across various datasets and network structures.
Low-bit deep neural networks (DNNs) become critical for embedded applications due to their low storage requirement and computing efficiency. However, they suffer much from the non-negligible accuracy drop. This paper proposes the stochastic quantization (SQ) algorithm for learning accurate low-bit DNNs. The motivation is due to the following observation. Existing training algorithms approximate the real-valued elements/filters with low-bit representation all together in each iteration. The quantization errors may be small for some elements/filters, while are remarkable for others, which lead to inappropriate gradient direction during training, and thus bring notable accuracy drop. Instead, SQ quantizes a portion of elements/filters to low-bit with a stochastic probability inversely proportional to the quantization error, while keeping the other portion unchanged with full-precision. The quantized and full-precision portions are updated with corresponding gradients separately in each iteration. The SQ ratio is gradually increased until the whole network is quantized. This procedure can greatly compensate the quantization error and thus yield better accuracy for low-bit DNNs. Experiments show that SQ can consistently and significantly improve the accuracy for different low-bit DNNs on various datasets and various network structures.