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Title: Non-uniform DNN Structured Subnets Sampling for Dynamic Inference
With the success of Deep Neural Networks (DNN), many recent works have been focusing on developing hardware accelerator for power and resource-limited system via model compression techniques, such as quantization, pruning, low-rank approximation and etc. However, almost all existing compressed DNNs are fixed after deployment, which lacks run-time adaptive structure to adapt to its dynamic hardware resource allocation, power budget, throughput requirement, as well as dynamic workload. As the countermeasure, to construct a novel run-time dynamic DNN structure, we propose a novel DNN sub-network sampling method via non-uniform channel selection for subnets generation. Thus, user can trade off between power, speed, computing load and accuracy on-the-fly after the deployment, depending on the dynamic requirements or specifications of the given system. We verify the proposed model on both CIFAR-10 and ImageNet dataset using ResNets, which outperforms the same sub-nets trained individually and other related works. It shows that, our method can achieve latency trade-off among 13.4, 24.6, 41.3, 62.1(ms) and 30.5, 38.7, 51, 65.4(ms) for GPU with 128 batch-size and CPU respectively on ImageNet using ResNet18.  more » « less
Award ID(s):
2005209 1931871
NSF-PAR ID:
10295337
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2020 57th ACM/IEEE Design Automation Conference (DAC)
Page Range / eLocation ID:
1 to 6
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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