Reducing computational energy consumption in cyber-physical systems (CPSs) has attracted considerable attention in recent years. Associated with energy consumption is a heating of the devices. Device failure rate increases exponentially with increase temperature, so that high energy consumption leads to a significant shortening of processor lifetime.

Reducing thermal stress without harming application safety and performance is the goal of this work. Our approach is to abort control tasks dispatch when this is judged, by a neural network, to not contribute to either safety or performance. This technique is orthogonal to others that have been used to reduce energy consumption such as dynamic voltage/frequency scaling and adaptive use of redundancy. Simulation experiments show that this approach leads to a further reduction in device aging when used in conjunction with these prior techniques.

近年来，减少网络物理系统（CPS）中的计算能耗已经引起了相当大的关注。与能量消耗相关的是设备的加热。随着温度的升高，设备故障率呈指数增长，因此高能耗导致处理器寿命显着缩短。

在不损害应用安全性和性能的情况下降低热应力是这项工作的目标。我们的方法是，当神经网络判断控制任务的派发不会对安全性或性能造成影响时，中止控制任务的派发。此技术与其他已用于降低能耗的技术正交，例如动态电压/频率缩放和自适应使用冗余。仿真实验表明，与这些现有技术结合使用时，这种方法可进一步降低器件老化。