You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In this section, we present our approach to speed up inference withquantized arithmetic. Our solution is tailored towards the hardwareoptions available at Google. To reduce quantization errors, additionalconstraints are added to our model during training so that it is quantizablewith minimal impact on the output of the model. That is, once amodel is trained with these additional constraints, it can be subsequentlyquantized without loss to translation quality. Our experimental results suggestthat those additional constraints do not hurt model convergence nor the qualityof a model once it has converged. It is worth emphasizing that during training of the model we use full-precisionfloating point numbers. The only constraints we add to the modelduring training are the clipping of the RNN accumulator values into[-\delta,\delta] and softmax logits into[-\gamma,\gamma]. \gamma is fixed to be at 25.0, while thevalue for \delta is gradually annealed from a generous bound of\delta=8.0 at the beginning of training, to a rather stringent boundof \delta=1.0 towards the end of training. At inference time,\delta is fixed at 1.0. Those additional constraints do not degrademodel convergence nor the decoding quality of the model when it hasconverged. In Figure 4, we compare the lossvs. steps for an unconstrained model (the blue curve) and a constrainedmodel (the red curve) on WMT’14 English-to-French. We can see thatthe loss for the constrained model is slightly better, possibly due toregularization roles those constraints play. Our solution strikes a good balance between efficiency andaccuracy. Since the computationally expensive operations (the matrixmultiplications) are done using 8-bit integer operations, ourquantized inference is quite efficient. Also, since error-sensitiveaccumulator values are stored using 16-bit integers, our solution isvery accurate and is robust to quantization errors. Table 1 shows that decoding using reducedprecision arithmetics on the TPU suffers a very minimal loss of 0.0072 onlog perplexity, and no loss on BLEU at all. This result matchesprevious work reporting that quantizing convolutional neuralnetwork models can retain most of the model quality.

Question: Is there a disadvantage to using low-precision arithmetic for inference, such as decreased inference accuracy?
Quantization models can perform slightly have lower results on neural network models, however in this paper authors performed some constraints during training so that's quantizable with minimal impact on the output of the model, the quantized model even performed slightly better than none-quantized training and they suggest it could be due to regularization roles those constraints had during training.