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.
翻译:低精确度格式证明是一种有效的方法,不仅可以减少记忆足迹,而且可以减少硬件资源和深层学习计算所消耗的能量。在这个前提下,假设数字格式似乎是IEEE浮动点的高度可行的替代物,但对神经网络培训的应用仍需进一步研究。一些初步结果显示,8位(甚至更小)假设可用于推断,16位用于培训,同时保持模型准确性。提出的研究旨在评价利用现实来培训深层革命神经网络的可行性。为此,开发了一个软件框架,在端到端培训和推论中使用模拟假设和要求。这一应用允许使用任何位大小、配置甚至混合精度,适合不同阶段的不同精确要求。获得的结果表明,8位假设可以在培训期间替代32位浮标,不会对由此造成的损失和准确性产生负面影响。