PISA architecture chip resource scheduling algorithm design and implementation
In order to speed up the efficiency in designing PISA, a series of resource constraints are imposed. The resource scheduling situation under data dependency, control dependency, and various resource dependency constraints is to be explored. In this regard, a mixed integer programming model is develo...
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| Vydáno v: | 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) s. 407 - 411 |
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| Hlavní autor: | |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
24.02.2023
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| Témata: | |
| On-line přístup: | Získat plný text |
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| Shrnutí: | In order to speed up the efficiency in designing PISA, a series of resource constraints are imposed. The resource scheduling situation under data dependency, control dependency, and various resource dependency constraints is to be explored. In this regard, a mixed integer programming model is developed and the optimization objective is determined to keep the number of occupied pipeline stages as short as possible. On the one hand, the data pre-processing work can be carried out, and the data can be transformed into a matrix of 0-1 variables to facilitate the control of the basic information in each module, and the processed data are judged to determine whether the decision variables can satisfy the constraints; on the other hand, the constraints limiting the objective function already in the problem are determined, and the corresponding equations are listed for the three different data dependencies of read-after-write, write-after-read, and write-after-write. At the same time to find the constraints implied by the problem itself, according to the linking relationship of the basic blocks in each board to adjust the order of the boards, due to the directed basic block linking information determines between adjacent boards, any basic block of the latter board must be linked with a certain basic block of the previous board, that is, the remaining hidden constraints can be found by the Big M method. After determining the complete decision variables, constraints and objective functions, a mixed integer programming model can be established to optimize the minimum number of occupied pipeline levels as the objective, and the results are calculated by using the lingo solver, and finally the results are optimized and verified by the heuristic algorithm - particle swarm algorithm to obtain the pipeline of problem one The number of basic blocks is the most when the number of levels is 4, and the corresponding number is 57, and the total number of pipeline levels is 53 at this time. |
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| DOI: | 10.1109/EEBDA56825.2023.10090793 |