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EnglishCapsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the traditional CNNs. However, CapsNets require extremely intense computations and are difficult to be deployed in their original form at the resource-constrained edge devices. This paper makes the first attempt to quantize CapsNet models, to enable their efficient edge implementations, by developing a specialized quantization framework for CapsNets. We evaluate our framework for several benchmarks. On a deep CapsNet model for the CIFAR10 dataset, the framework reduces the memory footprint by 6.2x, with only 0.15% accuracy loss. We will open-source our framework at https://git.io/JvDIF.
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks / Marchisio, Alberto; Bussolino, Beatrice; Colucci, Alessio; Martina, Maurizio; Masera, Guido; Shafique, Muhammad. - ELETTRONICO. - 1:(2020), pp. 1-6. ((Intervento presentato al convegno 2020 57th ACM/IEEE Design Automation Conference (DAC) tenutosi a San Francisco, CA, USA, USA nel 20-24 July 2020 [10.1109/DAC18072.2020.9218746].
| Titolo: | Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning c...apabilities in machine learning tasks, like image classification, compared to the traditional CNNs. However, CapsNets require extremely intense computations and are difficult to be deployed in their original form at the resource-constrained edge devices. This paper makes the first attempt to quantize CapsNet models, to enable their efficient edge implementations, by developing a specialized quantization framework for CapsNets. We evaluate our framework for several benchmarks. On a deep CapsNet model for the CIFAR10 dataset, the framework reduces the memory footprint by 6.2x, with only 0.15% accuracy loss. We will open-source our framework at https://git.io/JvDIF. | |
| ISBN: | 978-1-7281-1085-1 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 095_3.pdf | Articolo principale | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
| Q-CapsNets.pdf | Versione autore | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2848255
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