The Posit Number System (PNS) was introduced by John L. Gustafson in 2017. The interesting properties of this novel format can be exploited under the scenario of deep neural networks. In this paper, we propose Deep PeNSieve, a framework for entirely performing both training and inference of deep neural networks employing the PNS. Furthermore, an 8-bit posit quantization approach using fused operations is introduced. In comparison with the state-of-the-art posit frameworks, the proposal has been able to train more complex networks than the feedforward ones, achieving similar accuracies as the floating-point format. The case of CIFAR-10 is especially remarkable, as 16-bit posits even obtain 4% higher top-1 for such dataset. Overall, results show that the proposed quantization approach can preserve model accuracy in the same manner as common quantization techniques.

Posit Number System（PNS）由John L. Gustafson于2017年推出。在深度神经网络的情况下，可以利用这种新颖格式的有趣特性。在本文中，我们提出了Deep PeNSieve，这是一个完全使用PNS进行深度神经网络的训练和推理的框架。此外，介绍了一种使用融合运算的8位位置量化方法。与最新的positive框架相比，该提案能够训练比前馈网络更复杂的网络，从而实现与浮点格式相似的准确性。CIFAR-10的情况尤其引人注目，因为16位位置甚至可以为此类数据集的top-1高4％。总体，