CAREER: Leveraging Heterogeneous Manycore Systems for Scalable Modeling, Simulation and Verification of Nanoscale Integrated Circuits

这个CAREER研究项目的目标是最好地释放新兴异构众核CPU-GPU计算平台的力量。这将需要革新下一代电子设计自动化（EDA）工具，以处理涉及数十亿元器件的前所未有的复杂电路，使其建模，分析和验证任务成为可能，这些任务将非常昂贵，甚至难以处理。在这项研究中获得的经验也可能有助于在科学和工程的其他领域使用计算的进步，从而影响到复杂系统建模和仿真，计算流体动力学，社会计算和系统生物学等领域。PI将促进本科生和代表性不足的学生研究，以及K-12教育推广，以激励学生追求先进的工程教育或在STEM领域的职业生涯。此外，PI将把研究成果纳入本科和研究生课程开发，并利用跨学科，工业和国际合作，有效地促进拟议的研究工作，并广泛传播成果。未来的纳米级集成电路（IC）子系统，如时钟分配，功率传输网络，嵌入式存储器阵列，以及模拟和混合信号系统，可能会达到前所未有的复杂性，涉及数十亿个电路组件，使其建模，分析和验证任务过于昂贵，难以与现有的EDA工具。另一方面，新兴的异构众核计算系统，诸如将几个大型但功耗高的通用处理器与大量更纤薄但更节能的图形处理器集成的众核CPU-GPU计算平台，理论上可以提供万亿次的计算能力。该提案旨在加速EDA研究向更节能的异构计算体系的范式转变。为此，PI将开发系统的硬件/软件方法，通过发明异构CAD算法和数据结构，以及利用特定于硬件和特定于域的运行时性能建模和优化方法，实现可扩展的集成电路建模，仿真和验证。

The goal of this CAREER research project is to best unleash the power of emerging heterogeneous manycore CPU-GPU computing platforms. This will require revolutionizing the next-generation Electronic Design Automation (EDA) tools to deal with unprecedented complexity of circuits involving billions of components, making possible their modeling, analysis and verification tasks which would be prohibitively expensive and even intractable with methods in use today. The experience acquired in this research is also likely to contribute to advances in the use of computing in other areas of science and engineering, thus impacting areas such as complex system modeling and simulation, computational fluid dynamics, social computing, and systems biology. The PI will promote undergraduate and underrepresented student research, as well as K-12 education outreach, to motivate students in pursuing advanced engineering education or a career in STEM areas. Additionally, the PI will integrate the research outcomes into undergraduate and graduate curriculum development, and leverage interdisciplinary, industrial and international collaborations to effectively facilitate the proposed research work and broadly disseminate the results. Future nanoscale Integrated Circuit (IC) subsystems, such as clock distributions, power delivery networks, embedded memory arrays, as well as analog and mixed-signal systems, may reach an unprecedented complexity involving billions of circuit components, making their modeling, analysis and verification tasks prohibitively expensive and intractable with existing EDA tools. On the other hand, emerging heterogeneous manycore computing systems, such as the manycore CPU-GPU computing platforms that integrate a few large yet power-consuming general purpose processors with massive number of much slimmer but more energy-efficient graphics processors, can theoretically delivery teraflops of computing power. The proposal aims to accelerate a paradigm shift in EDA research to more energy-efficient heterogeneous computing regimes. Towards this end, the PI will develop systematic hardware/software approaches to achieve scalable integrated circuit modeling, simulation and verifications by inventing heterogeneous CAD algorithms and data structures, as well as exploiting hardware-specific and domain-specific runtime performance modeling and optimization approaches.