Automatic End-to-End Joint Optimization for Kernel Compilation on DSPs
Digital signal processors (DSPs) commonly adopt VLIW-SIMD architecture and are extensively applied in most compute-heavy embedded sensing applications. The performances for DSP kernels rely heavily on compilations and handwritten optimizations. Hand-crafted methods suffer from heavy burden on progra...
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| Vydáno v: | 2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6 |
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IEEE
09.07.2023
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| Abstract | Digital signal processors (DSPs) commonly adopt VLIW-SIMD architecture and are extensively applied in most compute-heavy embedded sensing applications. The performances for DSP kernels rely heavily on compilations and handwritten optimizations. Hand-crafted methods suffer from heavy burden on programmers, while state-of-the-art automatic compilation methods always focus more on a certain aspect (tiling or auto-vectorization), lacking of global and sequential vision on the intact compilation optimization process. It still requires empirical adjustments by programmers in the actual scenario.In order to release programmers from kernel tuning, we propose JOKer, an automatic end-to-end multi-level code generator for kernel joint optimization on DSPs. JOKer integrates means of optimizations in compiling process and provides an end-to-end workflow for performance tuning. It explores compilation configurations through a reinforcement learning based agent for global optimal solution and generates high performance kernel codes for DSPs automatically. |
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| AbstractList | Digital signal processors (DSPs) commonly adopt VLIW-SIMD architecture and are extensively applied in most compute-heavy embedded sensing applications. The performances for DSP kernels rely heavily on compilations and handwritten optimizations. Hand-crafted methods suffer from heavy burden on programmers, while state-of-the-art automatic compilation methods always focus more on a certain aspect (tiling or auto-vectorization), lacking of global and sequential vision on the intact compilation optimization process. It still requires empirical adjustments by programmers in the actual scenario.In order to release programmers from kernel tuning, we propose JOKer, an automatic end-to-end multi-level code generator for kernel joint optimization on DSPs. JOKer integrates means of optimizations in compiling process and provides an end-to-end workflow for performance tuning. It explores compilation configurations through a reinforcement learning based agent for global optimal solution and generates high performance kernel codes for DSPs automatically. |
| Author | Zhao, Xiaolei Wen, Mei Shi, Yang Chen, Zhaoyun Zhang, Chunyun |
| Author_xml | – sequence: 1 givenname: Xiaolei surname: Zhao fullname: Zhao, Xiaolei organization: National University of Defense Technology,Changsha,China – sequence: 2 givenname: Zhaoyun surname: Chen fullname: Chen, Zhaoyun email: chenzhaoyun@nudt.edu.cn organization: National University of Defense Technology,Changsha,China – sequence: 3 givenname: Yang surname: Shi fullname: Shi, Yang organization: National University of Defense Technology,Changsha,China – sequence: 4 givenname: Mei surname: Wen fullname: Wen, Mei organization: National University of Defense Technology,Changsha,China – sequence: 5 givenname: Chunyun surname: Zhang fullname: Zhang, Chunyun organization: National University of Defense Technology,Changsha,China |
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| Snippet | Digital signal processors (DSPs) commonly adopt VLIW-SIMD architecture and are extensively applied in most compute-heavy embedded sensing applications. The... |
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| SubjectTerms | Auto Tuning Code Generation Codes Digital signal processors DSP Generators Kernel Compilation Q-learning Reinforcement Learning Sensors Signal processing algorithms Space exploration |
| Title | Automatic End-to-End Joint Optimization for Kernel Compilation on DSPs |
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