Researcher: Zhuo Feng, PI, Associate Professor, Electrical and Computer Engineering
Sponsor: National Science Foundation
Amount of Support: $400,000
Duration of Support: 6 years
Abstract: 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.
Publications:
Xueqian Zhao*, Lengfei Han*, and Zhuo Feng. “A Performance-Guided Graph Sparsification Approach to Scalable and Robust SPICE-Accurate Integrated Circuit Simulations,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2015.
Lengfei Han and Zhuo Feng. “Transient-simulation guided graph sparsification approach to scalable harmonic balance (HB) analysis of post-layout RF circuits leveraging heterogeneous CPU-GPU computing systems,” Proceedings of ACM/IEEE Design Automation Conference (DAC), 2015.
Xueqian Zhao, Lengfei Han, and Zhuo Feng. “A Performance-Guided Graph Sparsification Approach to Scalable and Robust SPICE-Accurate Integrated Circuit Simulations,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2015.
Lengfei Han and Zhuo Feng. “TinySPICE Plus: Scaling Up Statistical SPICE Simulations on GPU Leveraging Shared-Memory Based Sparse Matrix Solution Techniques,” IEEE/ACM International Conference on Computer-Aided Design, 2016.
Zhuo Feng. “Spectral Graph Sparsification in Nearly-Linear Time Leveraging Efficient Spectral Perturbation Analysis,” ACM/IEEE Design Automation Conference, 2016.
Zhiqiang Zhao, Yongyu Wang, and Zhuo Feng. “SAMG: Sparsified Algebraic Multigrid for Solving Large Symmetric Diagonally Dominant (SDD) Matrices,” IEEE/ACM International Conference on Computer-Aided Design, 2017.
Zhiqiang Zhao, Zhuo Feng. “A Spectral Graph Sparsication Approach to Scalable Vectorless Power Grid Integrity Verication,” ACM/IEEE Design Automation Conference (DAC), 2017.