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.
EnglishThe deployment of Convolutional Neural Networks (CNNs) on resource-constrained IoT devices calls for accurate model re-sizing and optimization. Among the proposed compression strategies, n-ary fixed-point quantization has proven effective in reducing both computational effort and memory footprint with no (or limited) accuracy loss. However, its use requires custom components and special memory allocation strategies which are not available and burdensome to implement on low-power/low-cost cores. In order to bridge this gap, this work introduces Virtual Quantization (VQ), a hardware-friendly compression method which allows to implement equivalent n ary CNNs on general purpose instruction-set architectures. The proposed VQ framework is validated for the IoT family of ARM MCUs (ARM Cortex-M) and tested with three different real-life applications (i.e. Image Classification, Keyword Spotting, Facial Expression Recognition).
Arbitrary-Precision Convolutional Neural Networks on Low-Power IoT Processors / Peluso, V.; Grimaldi, M.; Calimera, A.. - 2019-(2019), pp. 142-147. ((Intervento presentato al convegno 27th IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2019 tenutosi a per nel 2019.
| Titolo: | Arbitrary-Precision Convolutional Neural Networks on Low-Power IoT Processors |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | The deployment of Convolutional Neural Networks (CNNs) on resource-constrained IoT devices calls ...for accurate model re-sizing and optimization. Among the proposed compression strategies, n-ary fixed-point quantization has proven effective in reducing both computational effort and memory footprint with no (or limited) accuracy loss. However, its use requires custom components and special memory allocation strategies which are not available and burdensome to implement on low-power/low-cost cores. In order to bridge this gap, this work introduces Virtual Quantization (VQ), a hardware-friendly compression method which allows to implement equivalent n ary CNNs on general purpose instruction-set architectures. The proposed VQ framework is validated for the IoT family of ARM MCUs (ARM Cortex-M) and tested with three different real-life applications (i.e. Image Classification, Keyword Spotting, Facial Expression Recognition). |
| ISBN: | 978-1-7281-3915-9 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 8920341.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2816978
Copyright © 2020