As the demand for efficient data processing escalates, reconfigurable analog hardware which implements novel analog compute paradigms, is promising for energy-efficient computing at the sensing and actuation boundaries. These analog computing platforms embed information in physical properties and then use the physics of materials, devices, and circuits to perform computation. These hardware platforms are more sensitive to nonidealities, such as noise and fabrication variations, than their digital counterparts and accrue high resource costs when programmable elements are introduced. Identifying resource-efficient analog system designs that mitigate these nonidealities is done manually today. While design optimization frameworks have been enormously successful in other fields, such as photonics, they typically either target linear dynamical systems that have closed-form solutions or target a specific differential equation system and then derive the solution through hand analysis. In both cases, time-domain simulation is no longer needed to predict hardware behavior. In contrast, described analog hardware platforms have nonlinear time-evolving dynamics that vary substantially from design to design, lack closed-form solutions, and require the optimizer to consider time explicitly. We present Shem, an optimization framework for analog systems. Shem leverages differentiation methods recently popularized to train neural ODEs to enable the optimization of analog systems that exhibit nonlinear dynamics, noise and mismatch, and discrete behavior. We evaluate Shem on oscillator-based pattern recognizer, CNN edge detector, and transmission-line security primitive design case studies and demonstrate it can improve designs. To our knowledge, the latter two design problems have not been optimized with automated methods before.

翻译：随着高效数据处理需求的不断增长，可重构模拟硬件——这种实现新型模拟计算范式的技术——在传感与执行边界处展现出实现高能效计算的巨大潜力。此类模拟计算平台将信息嵌入物理特性中，并利用材料、器件及电路的物理特性执行计算。与数字硬件相比，这些硬件平台对非理想因素（如噪声与制造偏差）更为敏感，且在引入可编程元件时会产生高昂的资源开销。目前，寻找能够缓解这些非理想因素且资源高效的模拟系统设计仍依赖于人工方式。尽管设计优化框架在光子学等其他领域已取得巨大成功，但这些框架通常要么针对具有闭式解的线性动态系统，要么针对特定微分方程系统并通过人工分析推导解。在这两种情况下，均不再需要时域仿真来预测硬件行为。相比之下，所描述的模拟硬件平台具有非线性时变动态特性，其特性因设计而异，缺乏闭式解，且需要优化器显式考虑时间维度。本文提出Shem——一个面向模拟系统的优化框架。Shem借鉴了近期在训练神经常微分方程中普及的微分方法，使得对呈现非线性动态、噪声与失配以及离散行为的模拟系统进行优化成为可能。我们在基于振荡器的模式识别器、CNN边缘检测器和传输线安全原语设计案例研究中评估Shem，并证明其能够改进设计。据我们所知，后两个设计问题此前从未通过自动化方法进行过优化。