Binary Input Layer: Training of CNN models with binary input data

Autor:	Dürichen, Robert, Rocznik, Thomas, Renz, Oliver, Peters, Christian
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Computer Science - Computational Complexity Statistics - Machine Learning I.2.6
Druh dokumentu:	Working Paper
Popis:	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. Comment: NeurIPS, 2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2), 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1812.03410 Zobrazit plný text záznamu View this record from Arxiv