Over the past several decades the dramatic increase in the availability of computational resources, coupled with the maturation of machine learning, has profoundly impacted sensor technology. In this Perspective, we discuss computational sensing with a focus on intelligent sensor system design. By leveraging inverse design and machine learning techniques, data acquisition hardware can be fundamentally redesigned to ‘lock-in’ to the optimal sensing data with respect to a user-defined cost function or design constraint. We envision a new generation of computational sensing systems that reduce the data burden while also improving sensing capabilities, enabling low-cost and compact sensor implementations engineered through iterative analysis of data-driven sensing outcomes. We believe that the methodologies discussed in this Perspective will permeate the design phase of sensing hardware, and thereby will fundamentally change and challenge traditional, intuition-driven sensor and readout designs in favour of application-targeted and perhaps highly non-intuitive implementations. Such computational sensors enabled by machine learning can therefore foster new and widely distributed applications that will benefit from ‘big data’ analytics and the internet of things to create powerful sensing networks, impacting various fields, including for example, biomedical diagnostics, environmental sensing and global health, among others.

在过去的几十年里，计算资源可用性的急剧增加，加上机器学习的成熟，对传感器技术产生了深远的影响。在这个视角中，我们讨论计算传感，重点是智能传感器系统设计。通过利用逆向设计和机器学习技术，可以从根本上重新设计数据采集硬件，以根据用户定义的成本函数或设计约束“锁定”最佳传感数据。我们设想了新一代的计算传感系统，可以减少数据负担，同时提高传感能力，通过对数据驱动的传感结果的迭代分析来实现低成本和紧凑的传感器实现。我们相信，本视角中讨论的方法将渗透到传感硬件的设计阶段，从而从根本上改变和挑战传统的、直觉驱动的传感器和读出设计，以支持面向应用程序且可能高度非直观的实现。因此，这种由机器学习支持的计算传感器可以促进新的广泛分布的应用程序，这些应用程序将受益于“大数据”分析和物联网，以创建强大的传感网络，影响各个领域，例如，生物医学诊断、环境传感和全球健康等。直觉驱动的传感器和读出设计，有利于以应用为目标且可能高度非直观的实现。因此，这种由机器学习支持的计算传感器可以促进新的广泛分布的应用程序，这些应用程序将受益于“大数据”分析和物联网，以创建强大的传感网络，影响各个领域，例如，生物医学诊断、环境传感和全球健康等。直觉驱动的传感器和读出设计有利于以应用为目标且可能高度非直观的实现。因此，这种由机器学习支持的计算传感器可以促进新的广泛分布的应用程序，这些应用程序将受益于“大数据”分析和物联网，以创建强大的传感网络，影响各个领域，例如，生物医学诊断、环境传感和全球健康等。