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arXiv · 2002.00623

Exponential discretization of weights of neural network connections in pre-trained neural networks

Magomed Yu. Malsagov·Emil M. Khayrov·Maria M. Pushkareva·Iakov M. Karandashev
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Abstract

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.

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Magomed Yu. Malsagov, Emil M. Khayrov, Maria M. Pushkareva, Iakov M. Karandashev. 2020-02-03. Exponential discretization of weights of neural network connections in pre-trained neural networks. https://arxiv.org/abs/2002.00623

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