  Recently, the number of parameters in DNNs has explosively increased, as
exemplified by LLMs (Large Language Models), making inference on small-scale
computers more difficult. Model compression technology is, therefore, essential
for integration into products. In this paper, we propose a method of
quantization-aware training. We introduce a novel normalization (Layer-Batch
Normalization) that is independent of the mini-batch size and does not require
any additional computation cost during inference. Then, we quantize the weights
by the scaled round-clip function with the weight standardization. We also
quantize activation functions using the same function and apply surrogate
gradients to train the model with both quantized weights and the quantized
activation functions. We call this method Magic for the age of Quantised DNNs
(MaQD). Experimental results show that our quantization method can be achieved
with minimal accuracy degradation.
