PORTO @ 
Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishMulti-FPGA platforms like Amazon Web Services F1 are perfect to accelerate multi-kernel pipelined applications, like Convolutional Neural Networks (CNNs). To reduce energy consumption, we propose to upload at runtime the best power-optimized CNN implementation for a given throughput constraint. Our design method gives the best number of parallel instances of each kernel, their allocation to the FPGAs, the number of powered-on FPGAs and their clock frequency. This is obtained by solving a mixed-integer, non-linear optimization problem that models power and performance of each component, as well as the duration of the computation phases—data transfer between a host CPU and the FPGA memory (typically DDR), data transfer between DDR and FPGA, and FPGA computation. The results show that the power saved compared to simply clock gating the fastest implementation is obviously very high, but it is also much more significant than simply scaling the frequency of the fastest implementation or replicating the slowest implementation on multiple FPGAs.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Titolo: | Power-Optimal Mapping of CNN Applications to Cloud-Based Multi-FPGA Platforms |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Rivista: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/TCSII.2020.2998284 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| upload.pdf | Main article | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| Lazarescu-Power.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2837760
Copyright © 2020