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We introduce FxpNet, a framework to train deep convolutional neural networks with low bit-width arithmetics in both forward pass and backward pass. During training FxpNet further reduces the bit-width of stored parameters (also known as primal parameters) by adaptively updating their fixed-point formats. These primal parameters are usually represented in the full resolution of floating-point values...
The world is experiencing a data revolution to discover knowledge in big data. Large scale neural networks are one of the mainstream tools of big data analytics. Processing big data with large scale neural networks includes two phases: the training phase and the operation phase. Huge computing power is required to support the training phase. And the energy efficiency (power efficiency) is one of the...
In the beginning of the 21st Century, based on the disadvantages of Bloom' educational objectives taxonomy, Robert J. Marzano carried out a new one. The core of the new taxonomy is thinking system, aiming to develop students' s high-order thinking skills. Based on the new taxonomy, this thesis analysed the essence of thinking training deeply from the four aspects:why do thinking training, what does...
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