A visionary computing paradigm is to train resource efficient neural networks on the edge using dedicated low-power accelerators instead of cloud infrastructures, eliminating communication overheads and privacy concerns. One promising resource-efficient approach for inference is binarized neural networks (BNNs), which binarize parameters and activations. However, training BNNs remains resource demanding. State-of-the-art BNN training methods, such as the binary optimizer (Bop), require to store

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... and update a large number of momentum values in the floating point (FP) format. In this work, we focus on memory-efficient FP encodings for the momentum values in Bop. To achieve this, we first investigate the impact of arbitrary FP encodings. When the FP format is not properly chosen, we prove that the updates of the momentum values can be lost and the quality of training is therefore dropped. With the insights, we formulate a metric to determine the number of unchanged momentum values in a training iteration due to the FP encoding. Based on the metric, we develop an algorithm to find FP encodings that are more memory-efficient than the standard FP encodings. In our experiments, the memory usage in BNN training is decreased by factors 2.47x, 2.43x, 2.04x, depending on the BNN model, with minimal accuracy cost (smaller than 1%) compared to using 32-bit FP encoding.