  To reduce random access memory (RAM) requirements and to increase speed of
recognition algorithms we consider a weight discretization problem for trained
neural networks. We show that an exponential discretization is preferable to a
linear discretization since it allows one to achieve the same accuracy when the
number of bits is 1 or 2 less. The quality of the neural network VGG-16 is
already satisfactory (top5 accuracy 69%) in the case of 3 bit exponential
discretization. The ResNet50 neural network shows top5 accuracy 84% at 4 bits.
Other neural networks perform fairly well at 5 bits (top5 accuracies of
Xception, Inception-v3, and MobileNet-v2 top5 were 87%, 90%, and 77%,
respectively). At less number of bits, the accuracy decreases rapidly.
