A Training Scheme for the Uncertain Neuromorphic Computing Chips

Uncertainty is a very important feature of the intelligence and helps the brain become a flexible, creative and powerful intelligent system. The crossbar-based neuromorphic computing chips, in which the computing is mainly performed by analog circuits, have the uncertainty and can be used to imitate the brain. However, most of the current deep neural networks have not taken the uncertainty of the neuromorphic computing chip into consideration. Therefore, their performances on the neuromorphic computing chips are not as good as on the original platforms (CPUs/GPUs). In this work, we proposed the uncertainty adaptation training scheme (UATS) that tells the uncertainty to the neural network in the training process. The experimental results show that the neural networks can achieve comparable inference performances on the uncertain neuromorphic computing chip compared to the results on the original platforms, and much better than the performances without this training scheme.