TY - GEN
T1 - Q-CapsNets
T2 - 57th ACM/IEEE Design Automation Conference, DAC 2020
AU - Marchisio, Alberto
AU - Bussolino, Beatrice
AU - Colucci, Alessio
AU - Martina, Maurizio
AU - Masera, Guido
AU - Shafique, Muhammad
N1 - Funding Information:
This work has been partially supported by the Doctoral College Resilient Embedded Systems
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - Capsule 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.
AB - Capsule 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.
KW - Capsule Networks
KW - Compression
KW - Quantization
UR - http://www.scopus.com/inward/record.url?scp=85093852544&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093852544&partnerID=8YFLogxK
U2 - 10.1109/DAC18072.2020.9218746
DO - 10.1109/DAC18072.2020.9218746
M3 - Conference contribution
AN - SCOPUS:85093852544
T3 - Proceedings - Design Automation Conference
BT - 2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 July 2020 through 24 July 2020
ER -