We investigate the compression of deep neural networks by quantizing their
weights and activations into multiple binary bases, known as multi-bit networks
(MBNs), which accelerate the inference and reduce the storage for the
deployment on low-resource mobile and embedded platforms. We propose Adaptive
Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to
achieve an average bitwidth below one-bit without notable loss in inference
accuracy. Unlike previous MBN quantization solutions that train a quantizer by
minimizing the error to reconstruct full precision weights, ALQ directly
minimizes the quantization-induced error on the loss function involving neither
gradient approximation nor full precision maintenance. ALQ also exploits
strategies including adaptive bitwidth, smooth bitwidth reduction, and
iterative trained quantization to allow a smaller network size without loss in
accuracy. Experiment results on popular image datasets show that ALQ
outperforms state-of-the-art compressed networks in terms of both storage and
accuracy. Code is available at this https URL

研究深度神经网络压缩，提出一种自适应损失感知量化方法 (ALQ) 来加速推理并在低资源移动和嵌入式平台上降低存储需求。实验结果表明，该方法在精度和存储方面优于最先进的压缩网络。