| Autor: |
Aleksandr Cariow, Janusz P. Papliński, Marta Makowska |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Applied Sciences, Vol 13, Iss 15, p 9004 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app13159004 |
| Popis: |
The paper introduces a range of efficient algorithmic solutions for implementing the fundamental filtering operation in convolutional layers of convolutional neural networks on fully parallel hardware. Specifically, these operations involve computing M inner products between neighbouring vectors generated by a sliding time window from the input data stream and an M-tap finite impulse response filter. By leveraging the factorisation of the Hankel matrix, we have successfully reduced the multiplicative complexity of the matrix-vector product calculation. This approach has been applied to develop fully parallel and resource-efficient algorithms for M values of 3, 5, 7, and 9. The fully parallel hardware implementation of our proposed algorithms achieves approximately a 30% reduction in embedded multipliers compared to the naive calculation methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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