Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical electronic design automation (EDA) problems suffer from the curse of dimensionality, i.e., the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g., 3-D field...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/110826 https://orcid.org/0000-0002-5880-3151 |
| _version_ | 1826214483794591744 |
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| author | Zhang, Zheng Batselier, Kim Liu, Haotian Daniel, Luca Wong, Ngai |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Zhang, Zheng Batselier, Kim Liu, Haotian Daniel, Luca Wong, Ngai |
| author_sort | Zhang, Zheng |
| collection | MIT |
| description | Many critical electronic design automation (EDA) problems suffer from the curse of dimensionality, i.e., the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g., 3-D field solvers discretizations and multirate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g., full-chip routing/placement and circuit sizing), or extensive process variations (e.g., variability /reliability analysis and design for manufacturability). The computational challenges generated by such high-dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents “tensor computation” as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage. |
| first_indexed | 2024-09-23T16:06:09Z |
| format | Article |
| id | mit-1721.1/110826 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:06:09Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1108262022-10-02T06:22:16Z Tensor Computation: A New Framework for High-Dimensional Problems in EDA Zhang, Zheng Batselier, Kim Liu, Haotian Daniel, Luca Wong, Ngai Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Zhang, Zheng Daniel, Luca Many critical electronic design automation (EDA) problems suffer from the curse of dimensionality, i.e., the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g., 3-D field solvers discretizations and multirate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g., full-chip routing/placement and circuit sizing), or extensive process variations (e.g., variability /reliability analysis and design for manufacturability). The computational challenges generated by such high-dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents “tensor computation” as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage. National Science Foundation (U.S.). Nano-Engineered Electronic Device Simulation AIM Photonics Massachusetts Institute of Technology. Greater China Fund for Innovation 2017-07-24T18:59:02Z 2017-07-24T18:59:02Z 2016-10 Article http://purl.org/eprint/type/JournalArticle 0278-0070 1937-4151 http://hdl.handle.net/1721.1/110826 Zhang, Zheng et al. “Tensor Computation: A New Framework for High-Dimensional Problems in EDA.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 36.4 (2017): 521–536. https://orcid.org/0000-0002-5880-3151 en_US http://dx.doi.org/10.1109/TCAD.2016.2618879 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT Web Domain |
| spellingShingle | Zhang, Zheng Batselier, Kim Liu, Haotian Daniel, Luca Wong, Ngai Tensor Computation: A New Framework for High-Dimensional Problems in EDA |
| title | Tensor Computation: A New Framework for High-Dimensional Problems in EDA |
| title_full | Tensor Computation: A New Framework for High-Dimensional Problems in EDA |
| title_fullStr | Tensor Computation: A New Framework for High-Dimensional Problems in EDA |
| title_full_unstemmed | Tensor Computation: A New Framework for High-Dimensional Problems in EDA |
| title_short | Tensor Computation: A New Framework for High-Dimensional Problems in EDA |
| title_sort | tensor computation a new framework for high dimensional problems in eda |
| url | http://hdl.handle.net/1721.1/110826 https://orcid.org/0000-0002-5880-3151 |
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