Simulation, Modeling and Design Automation for High-Performance Chip, Package and Systems

高性能芯片、封装和系统的仿真、建模和设计自动化

• 批准号: RGPIN-2020-06095
• 负责人: Khazaka, Roni
• 金额: $ 2.4万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718235
• 关键词: Simulation; Modeling; Design; Automation; Performance

In recent years, Microsystems have become ubiquitous, complex and increasingly inexpensive, and the availability of Electronic Design Automation tools was a necessary prerequisite for the success of this multibillion dollar industry. At the same time, the complexity of such systems has increased exponentially. A typical microchip now contains billions of transistors and many computing cores. Also, microsystems often include microelectromechanical systems (MEMS), various sensors, and optical interconnects to form complex multiphysics microsystems. At the same time, this complexity and corresponding increase in performance is being achieved without considerable increases in cost. Indeed, while the cost of the most advanced CPUs remains at a premium, it is now possible to obtain simpler yet still relatively powerful computers at a fraction of the cost of a few years ago. Some of the key enablers of this industry are Electronic Design Automation (EDA) tools. Such tools allow for systematic design flows, and the ability to design and manage highly complex systems while obeying increasingly fast product update cycles. In this research program, we aim to address critical strategic problems related to signal integrity and power integrity of high performance chip, package and systems from a simulation and design automation perspective. At the same time we will take into account modern multicore computing architectures by developing algorithms that can be readily computed in parallel. More specifically we will focus on the following general themes: 1. Model Order Reduction (MOR): The general idea behind model reduction is that large very complex systems with many degrees of freedoms (ranging into the millions) often have a relatively small number of dominant modes. Our goal is to develop MOR algorithms that reduce large problems into smaller ones, such that they can be efficiently simulated while maintaining acceptable accuracy. An important focus of this work is to develop techniques to reduce a large system into many smaller systems that can be solved in parallel thus enabling the use of parallel computing. 2. Blackbox modeling based on measured and simulated parameters: In this thrust of our research we will focus on automated model generation. For many practical applications it is very difficult to develop physicsbased mathematical models. We will develop tools and algorithms for automatically obtaining mathematical models of power and signal interconnect structures based on measured or simulated parameters. 3. Machine Learning Methodology: In recent years there have been considerable advances in the machine learning field. This area has much in common with neural networksbased modeling and surrogate modeling of complex electrical systems. We plan to develop machine learning inspired algorithms and methodologies for signal and power integrity simulation and optimization.

近年来，微系统已经变得无处不在，复杂和复杂。 越来越便宜，电子设计自动化 工具是这个数十亿美元的成功的必要先决条件 行业与此同时，这类系统的复杂性也有所增加 呈指数增长一个典型的微芯片现在包含数十亿个晶体管和许多 计算核心此外，微系统通常包括微机电系统。 微机电系统（MEMS）、各种传感器和光学互连，以形成复杂的 多物理场微系统与此同时，这种复杂性和 性能的相应提高， 成本增加。事实上，虽然最先进的CPU的成本仍然处于 溢价，现在可以获得更简单但仍然相对强大的 计算机的成本是几年前的一小部分。一些关键的推动因素 电子设计自动化（EDA）工具。这些工具允许 系统设计流程，以及设计和管理高度 复杂的系统，同时遵守越来越快的产品更新周期。 在这项研究计划中，我们的目标是解决关键的战略问题， 高性能芯片、封装 从仿真和设计自动化的角度来看系统。与此同时 我们将考虑现代多核计算架构， 开发可以并行计算的算法。更 具体而言，我们将侧重于以下一般主题： 1.模型降阶（莫尔）：模型降阶背后的一般思想是 具有许多自由度的大型非常复杂的系统（范围包括 数百万个）通常具有相对较少数量的主导模式。我们的目标 是开发莫尔算法，将大问题简化为小问题，例如 它们可以被有效地模拟，同时保持可接受的精度。一个 这项工作的一个重要重点是开发技术，以减少一个大的系统 许多更小的系统，可以并行解决，从而使使用 并行计算 2.基于测量和模拟参数的黑盒建模： 我们的研究重点是自动模型生成。对于许多 实际应用中，很难开发基于物理的 数学 模型我们将开发工具和算法， 功率和信号互连结构的数学模型 参数 3.机器学习方法：近年来， 机器学习领域的进步。这个地区有很多共同点， 基于神经网络 复杂电气系统的建模和代理建模 系统.我们计划开发受机器学习启发的算法， 用于信号和电源完整性仿真和优化的方法。