We thank the reviewers for insightful comments. Y our recognition of our work is much appreciated. The longer they are kept, the higher the number of bit flips they will suffer from. This easily results in a high fault rate (e.g. With that said, we consider extending our protection approaches to lower bit width as future work.

However, arranging weights such that their spatial distribution is fixed (e.g.

Tensorflow is a low-level deep learning package which requires users to deal with many complicated elements to construct a successful model. However, tensorflow is also powerful for production that's why most companies choose tensorflow as their major platforms. On the other hand, Keras provides a user-friendly API to help users quickly build complicated deep learning models but it is not appropriate for making products. Can we build our models in Keras and output it to tensorflow compatiable format (Protocol Buffers .pb In this tutorial, I will show to how to make it step-by-step.