The ability to estimate the uncertainty of predictions made by a neural network is essential when applying neural networks to tasks such as medical diagnosis and autonomous vehicles. The approach is of particular relevance when deploying the networks on devices with limited hardware resources, but existing competency-aware neural networks largely ignore any resource constraints. Here we examine the relationship between hardware platforms and the competency awareness of a neural network. We highlight the impact of two key areas of hardware development — increasing memory size of accelerator architectures and device-to-device variation in the emerging devices typically used in in-memory computing — on uncertainty estimation quality. We also consider the challenges that developments in uncertainty estimation methods impose on hardware designs. Finally, we explore the innovations required in terms of hardware, software, and hardware–software co-design in order to build future competency-aware neural networks.

当将神经网络应用于诸如医疗诊断和自动驾驶汽车之类的任务时，估计由神经网络做出的预测的不确定性的能力至关重要。当在硬件资源有限的设备上部署网络时，该方法特别有用，但是现有的具有胜任能力的神经网络在很大程度上忽略了任何资源限制。在这里，我们检查了硬件平台和神经网络能力意识之间的关系。我们重点介绍了硬件开发的两个关键领域-增加加速器体系结构的内存大小和内存中计算中通常使用的新兴设备中的设备间差异-对不确定性评估质量的影响。我们还考虑不确定性估计方法的发展给硬件设计带来的挑战。