Using low-precision data types, like the Brain Floating Point 16 (BF16) format, can reduce Convolutional Neural Networks (CNNs) memory usage in edge devices without significantly affecting their accuracy. Adding in-parameter zero-space Error Correction Codes (ECCs) can enhance the robustness of BF16-based CNNs. However, implementing this technique raises practical questions. For instance, when the available invariant1 and non-significant2 bits in parameters for error correction are sufficient for the required protection level, the proper selection and combination of these bits become crucial. On the other hand, if the set of available bits is inadequate, converting nearly invariant bits to invariants might be considered. These decisions impact ECC decoder complexity and may affect the overall CNN performance. This report examines such implications using Lenet-5 and GoogLenet as case studies.