UniCoS: A Unified Neural and Accelerator Co-Search Framework for CNNs and ViTs
Current algorithm-hardware co-search works often suffer from lengthy training times and inadequate exploration of hardware design spaces, leading to suboptimal performance. This work introduces UniCoS, a unified framework for co-optimizing neural networks and accelerators for CNNs and Vision Transfo...
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| Vydáno v: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 6 |
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IEEE
22.06.2025
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| Abstract | Current algorithm-hardware co-search works often suffer from lengthy training times and inadequate exploration of hardware design spaces, leading to suboptimal performance. This work introduces UniCoS, a unified framework for co-optimizing neural networks and accelerators for CNNs and Vision Transformers (ViTs). By introducing a novel training-free proxy that evaluates accuracy within seconds and a clustering-based algorithm for exploring heterogeneous dataflows, UniCoS efficiently navigates the design spaces of both architectures. Experimental results demonstrate that the solutions generated by UniCoS consistently surpass state-of-the-art (SOTA) methods (e.g., 3.54 \times energy-delay product (EDP) improvement with a 1.76 \% higher accuracy on ImageNet) while requiring notably reduced search time (up to 48 \times, \sim 3 hours). The code is available at https://github.com/mine7777/Unicos.git. |
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| AbstractList | Current algorithm-hardware co-search works often suffer from lengthy training times and inadequate exploration of hardware design spaces, leading to suboptimal performance. This work introduces UniCoS, a unified framework for co-optimizing neural networks and accelerators for CNNs and Vision Transformers (ViTs). By introducing a novel training-free proxy that evaluates accuracy within seconds and a clustering-based algorithm for exploring heterogeneous dataflows, UniCoS efficiently navigates the design spaces of both architectures. Experimental results demonstrate that the solutions generated by UniCoS consistently surpass state-of-the-art (SOTA) methods (e.g., 3.54 \times energy-delay product (EDP) improvement with a 1.76 \% higher accuracy on ImageNet) while requiring notably reduced search time (up to 48 \times, \sim 3 hours). The code is available at https://github.com/mine7777/Unicos.git. |
| Author | Zhou, Xuehai Tang, Cheng Wen, Hongbing Gong, Lei Wang, Chao Lou, Wenqi Qin, Yunji Fu, Wei |
| Author_xml | – sequence: 1 givenname: Wei surname: Fu fullname: Fu, Wei organization: University of Science and Technology of China,China – sequence: 2 givenname: Wenqi surname: Lou fullname: Lou, Wenqi organization: Suzhou Institute of Advanced Research, University of Science and Technology of China,China – sequence: 3 givenname: Cheng surname: Tang fullname: Tang, Cheng organization: University of Science and Technology of China,China – sequence: 4 givenname: Hongbing surname: Wen fullname: Wen, Hongbing organization: University of Science and Technology of China,China – sequence: 5 givenname: Yunji surname: Qin fullname: Qin, Yunji organization: University of Science and Technology of China,China – sequence: 6 givenname: Lei surname: Gong fullname: Gong, Lei email: louwenqi@ustc.edu.cn organization: University of Science and Technology of China,China – sequence: 7 givenname: Chao surname: Wang fullname: Wang, Chao email: cswang@ustc.edu.cn organization: University of Science and Technology of China,China – sequence: 8 givenname: Xuehai surname: Zhou fullname: Zhou, Xuehai organization: University of Science and Technology of China,China |
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| Snippet | Current algorithm-hardware co-search works often suffer from lengthy training times and inadequate exploration of hardware design spaces, leading to suboptimal... |
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| SubjectTerms | Accuracy Computer architecture Design automation Hardware acceleration hardware software co-exploration heterogeneous multi-core architecture Navigation Neural networks Software Space exploration Training Transformers zero-shot proxy |
| Title | UniCoS: A Unified Neural and Accelerator Co-Search Framework for CNNs and ViTs |
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