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• W3033586799 abstract "Convolutional Neural Networks (CNNs) have been widely used in many fields. However, the training process costs much energy and time, in which the convolution operations consume the major part. In this paper, we propose a fixed-point training framework, in order to reduce the data bit-width for the convolution multiplications. Firstly, we propose two constrained group-wise scaling methods that can be implemented with low hardware cost. Secondly, to overcome the challenge of trading off overflow and rounding error, a shiftable fixed-point data format is used in this framework. Finally, we propose a double-width deployment technique to boost inference performance with the same bit-width hardware multiplier. The experimental results show that the input data of convolution in the training process can be quantized to 2-bit for CIFAR-10 dataset, 6-bit for ImageNet dataset, with negligible accuracy degradation. Furthermore, our fixed-point train-ing framework has the potential to save at least 75% energy of the computation in the training process." @default.
• W3033586799 created "2020-06-12" @default.
• W3033586799 creator A5017721496 @default.
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• W3033586799 date "2020-06-04" @default.
• W3033586799 modified "2023-09-24" @default.
• W3033586799 title "Towards Lower Bit Multiplication for Convolutional Neural Network Training." @default.
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• W3033586799 hasPublicationYear "2020" @default.
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