Advancing DSP into HPC, AI, and beyond: challenges, mechanisms, and future directions
| Autor: | Yang Guo, Liu Chang, Sheng Liu, Zhang Yang, Chen Li, Yuanwu Lei, Yaohua Wang, Jian Zhang |
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| Rok vydání: | 2021 |
| Předmět: |
020203 distributed computing
Digital signal processor Scope (project management) business.industry Computer science 02 engineering and technology 020202 computer hardware & architecture Computer architecture Power consumption 0202 electrical engineering electronic engineering information engineering Key (cryptography) Milestone (project management) General Earth and Planetary Sciences Orchestration (computing) business Performance computing Digital signal processing General Environmental Science |
| Zdroj: | CCF Transactions on High Performance Computing. 3:114-125 |
| ISSN: | 2524-4930 2524-4922 |
| DOI: | 10.1007/s42514-020-00057-2 |
| Popis: | Digital Signal Processors (DSPs) have been widely used in embedded domains, delivering high performance with ultra-low power consumption. Such promises make it attractive for more domains that DSP was not an option before. To show how DSP lives up to these promises, we review two milestone DSPs: FT-Matrix and FT-Matrix2, which are designed by National University of Defense Technology with the purpose of advancing DSPs into the era of higher performance computing, AI, and even beyond. We demonstrate that the key challenges lie in the orchestration of huge computation resources and efficient data supply sub-system design. We show the key mechanisms in both FT-Matrix and FT-Matrix2 targeting these challenges, and also come up with possible future directions for enabling DSPs for a wider scope of applications. |
| Databáze: | OpenAIRE |
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