TY - GEN
T1 - BPNet
T2 - 57th ACM/IEEE Design Automation Conference, DAC 2020
AU - Park, Kyungchul
AU - Oh, Chanyoung
AU - Yi, Youngmin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Recently, there have been attempts to execute the neural network conditionally with auxiliary classifiers allowing early termination depending on the difficulty of the input, which can reduce the execution time or energy consumption without any or with negligible accuracy decrease. However, previous studies do not consider how many or where the auxiliary classifiers, or branches, should be added in a systematic fashion. In this paper, we propose Branch-pruned Conditional Neural Network (BPNet) and its methodology in which the time-accuracy tradeoff for the conditional neural network can be found systematically. We applied BPNet to SqueezeNet, ResNet-20, and VGG-16 with CIFAR-10 and 100. BPNet achieves up to 3.15× of speedup without any accuracy drop compared to the base networks.
AB - Recently, there have been attempts to execute the neural network conditionally with auxiliary classifiers allowing early termination depending on the difficulty of the input, which can reduce the execution time or energy consumption without any or with negligible accuracy decrease. However, previous studies do not consider how many or where the auxiliary classifiers, or branches, should be added in a systematic fashion. In this paper, we propose Branch-pruned Conditional Neural Network (BPNet) and its methodology in which the time-accuracy tradeoff for the conditional neural network can be found systematically. We applied BPNet to SqueezeNet, ResNet-20, and VGG-16 with CIFAR-10 and 100. BPNet achieves up to 3.15× of speedup without any accuracy drop compared to the base networks.
KW - And design space exploration
KW - Conditional neural networks
KW - Deep neural networks (DNNs)
KW - Embedded systems
UR - http://www.scopus.com/inward/record.url?scp=85093927085&partnerID=8YFLogxK
U2 - 10.1109/DAC18072.2020.9218545
DO - 10.1109/DAC18072.2020.9218545
M3 - Conference contribution
AN - SCOPUS:85093927085
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 -
