Design of cyber-physical systems (CPSs) is a challenging task that involves searching over a large search space of various CPS configurations and possible values of components composing the system. Hence, there is a need for sample-efficient CPS design space exploration to select the system architecture and component values that meet the target system requirements. We address this challenge by formulating CPS design as a multi-objective optimization problem and propose DISPATCH, a two-step methodology for sample-efficient search over the design space. First, we use a genetic algorithm to search over discrete choices of system component values for architecture search and component selection or only component selection and terminate the algorithm even before meeting the system requirements, thus yielding a coarse design. In the second step, we use an inverse design to search over a continuous space to fine-tune the component values and meet the diverse set of system requirements. We use a neural network as a surrogate function for the inverse design of the system. The neural network, converted into a mixed-integer linear program, is used for active learning to sample component values efficiently in a continuous search space. We illustrate the efficacy of DISPATCH on electrical circuit benchmarks: two-stage and three-stage transimpedence amplifiers. Simulation results show that the proposed methodology improves sample efficiency by 5-14x compared to a prior synthesis method that relies on reinforcement learning. It also synthesizes circuits with the best performance (highest bandwidth/lowest area) compared to designs synthesized using reinforcement learning, Bayesian optimization, or humans.

中文翻译：

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DISPATCH：信息物理系统的设计空间探索

网络物理系统 (CPS) 的设计是一项具有挑战性的任务，涉及在各种 CPS 配置的大型搜索空间和组成系统的组件的可能值中进行搜索。因此，需要对样本高效的 CPS 设计空间进行探索，以选择满足目标系统要求的系统架构和组件值。我们通过将 CPS 设计制定为多目标优化问题来解决这一挑战，并提出 DISPATCH，这是一种在设计空间上进行样本高效搜索的两步方法。首先，我们使用遗传算法来搜索系统组件值的离散选择，用于架构搜索和组件选择或仅组件选择，甚至在满足系统要求之前终止算法，从而产生粗略的设计。第二步，我们使用逆向设计来搜索连续空间以微调组件值并满足不同的系统要求。我们使用神经网络作为系统逆向设计的替代函数。转换为混合整数线性程序的神经网络用于主动学习，以在连续搜索空间中有效地对组件值进行采样。我们说明了 DISPATCH 在电路基准上的功效：两级和三级跨阻放大器。仿真结果表明，与依赖强化学习的先前合成方法相比，所提出的方法将样本效率提高了 5-14 倍。与使用强化学习、贝叶斯优化、