Compute-in-memory based binary neural networks or CiM-BNNs offer high energy/area efficiency for the design of edge deep neural network (DNN) accelerators, with only a mild accuracy reduction. However, for successful deployment, the design of CiM-BNNs must consider challenges such as memory faults and data security that plague existing DNN accelerators. In this work, we aim to mitigate both these problems simultaneously by proposing BNN-Flip, a training-free weight transformation algorithm that not only enhances the fault tolerance of CiM-BNNs but also protects them from weight theft attacks. BNN-Flip inverts the rows and columns of the BNN weight matrix in a way that reduces the impact of memory faults on the CiM-BNN's inference accuracy, while preserving the correctness of the CiM operation. Concurrently, our technique encodes the CiM-BNN weights, securing them from weight theft. Our experiments on various CiM-BNNs show that BNN-Flip achieves an inference accuracy increase of up to 10.55% over the baseline (i.e. CiM-BNNs not employing BNN-Flip) in the presence of memory faults. Additionally, we show that the encoded weights generated by BNN-Flip furnish extremely low (near 'random guess') inference accuracy for the adversary attempting weight theft. The benefits of BNN-Flip come with an energy overhead of < 3%.
