Low-precision formats have proven to be an efficient way to reduce not only the memory footprint but also the hardware resources and power consumption of deep learning computations. Under this premise, the posit numerical format appears to be a highly viable substitute for the IEEE floating-point, but its application to neural networks training still requires further research. Some preliminary results have shown that 8-bit (and even smaller) posits may be used for inference and 16-bit for training, while maintaining the model accuracy. The presented research aims to evaluate the feasibility to train deep convolutional neural networks using posits. For such purpose, a software framework was developed to use simulated posits and quires in end-to-end training and inference. This implementation allows using any bit size, configuration, and even mixed precision, suitable for different precision requirements in various stages. The obtained results suggest that 8-bit posits can substitute 32-bit floats during training with no negative impact on the resulting loss and accuracy.

中文翻译：

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PositNN：使用混合低精度Posit训练深度神经网络

低精度格式已被证明是一种有效的方法，不仅可以减少内存占用，而且可以减少深度学习计算的硬件资源和功耗。在此前提下，正数值格式似乎是IEEE浮点数的高度可行的替代方法，但其在神经网络训练中的应用仍需进一步研究。一些初步结果表明，在保持模型准确性的同时，可以使用8位（甚至更小）的位置进行推断，而使用16位的位置进行训练。提出的研究旨在评估使用posits训练深度卷积神经网络的可行性。为此，开发了一个软件框架，以在端到端的训练和推理中使用模拟的假设和要求。此实现允许使用任何位大小，配置，甚至混合精度，适用于各个阶段的不同精度要求。获得的结果表明，在训练过程中8位位置可以代替32位浮点，而不会对由此产生的损失和准确性产生负面影响。