Technically complex, this paper has numerous acronyms that are commonly used in specialist areas like electronics and engineering. However, the topic of DeepBurning can be summarized as a design automation tool that allows application developers the option to build a learning accelerator for specific neural networks. This process uses field-programmable gate arrays (FPGAs) that are designed to be modified and configured to suit problems in areas like machine learning and artificial learning.

In the paper, the DeepBurning framework is evaluated using eight neural networks, with comparisons of performance, power consumption, and accuracy. In conclusion, the authors indicate they have proved that DeepBurning enables an instant generation of hardware and software solutions for specific neural networks.

This paper and the general topic are not for the layperson and would only be of interest to industry-specific experts and academics within this field.