  For the efficient execution of deep convolutional neural networks (CNN) on
edge devices, various approaches have been presented which reduce the bit width
of the network parameters down to 1 bit. Binarization of the first layer was
always excluded, as it leads to a significant error increase. Here, we present
the novel concept of binary input layer (BIL), which allows the usage of binary
input data by learning bit specific binary weights. The concept is evaluated on
three datasets (PAMAP2, SVHN, CIFAR-10). Our results show that this approach is
in particular beneficial for multimodal datasets (PAMAP2) where it outperforms
networks using full precision weights in the first layer by 1:92 percentage
points (pp) while consuming only 2 % of the chip area.
