ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems (doi:10.21979/N9/IRNJ4I)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
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Document Description

Citation

Title:

ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems

Identification Number:

doi:10.21979/N9/IRNJ4I

Distributor:

DR-NTU (Data)

Date of Distribution:

2022-03-07

Version:

1

Bibliographic Citation:

Shuo, Huai; Liu, Weichen, 2022, "ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems", https://doi.org/10.21979/N9/IRNJ4I, DR-NTU (Data), V1, UNF:6:+jmG0vdNb5kvT4IJ3OJAeA== [fileUNF]

Study Description

Citation

Title:

ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems

Identification Number:

doi:10.21979/N9/IRNJ4I

Authoring Entity:

Shuo, Huai (Nanyang Technological University)

Liu, Weichen (Nanyang Technological University)

Software used in Production:

Python

Grant Number:

Singapore Government through the Industry Alignment Fund - Industry Collaboration Projects Grant (I1801E0028)

Distributor:

DR-NTU (Data)

Access Authority:

Shuo, Huai

Depositor:

Shuo, Huai

Date of Deposit:

2022-03-07

Holdings Information:

https://doi.org/10.21979/N9/IRNJ4I

Study Scope

Keywords:

Engineering, Engineering, ZeroBN, Compact Learning

Abstract:

Codes and data for "ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems"

Kind of Data:

program source code

Methodology and Processing

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Identification Number:

10.1109/DAC18074.2021.9586309

Bibliographic Citation:

Huai, S., Zhang, L., Liu, D., Liu, W., & Subramaniam, R. (2021, December). ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems. In 2021 58th ACM/IEEE Design Automation Conference (DAC) (pp. 151-156). IEEE.

Citation

Identification Number:

10356/155572

Bibliographic Citation:

Huai, S., Zhang, L., Liu, D., Liu, W. & Subramaniam, R. (2021). ZeroBN : learning compact neural networks for latency-critical edge systems. 2021 58th ACM/IEEE Design Automation Conference (DAC), 151-156.

File Description--f86101

File: y_i.tab

  • Number of cases: 5503

  • No. of variables per record: 1

  • Type of File: text/tab-separated-values

Notes:

UNF:6:+jmG0vdNb5kvT4IJ3OJAeA==

Variable Description

List of Variables:

Variables

0.00000015148668808251300000000

f86101 Location:

Summary Statistics: StDev 0.12967455202424852; Mean 0.06845981380958771; Valid 5503.0; Min. 7.58904258879589E-11; Max. 1.23619699478149

Variable Format: numeric

Notes: UNF:6:+jmG0vdNb5kvT4IJ3OJAeA==

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channel_selection.py

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text/x-python

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channel_selection.py

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text/x-python

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densenet.py

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text/x-python

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environment.yml

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application/octet-stream

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framework.png

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image/png

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googlenet.py

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text/x-python

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googlenet_torch.py

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googlenet_torch_scale.py

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imagenet100.txt

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__init__.py

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__init__.py

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__init__.py

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load_mat.py

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predictor.py

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preresnet.py

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text/x-python

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text/markdown

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resnet_new.py

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resnet_torch.py

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text/x-python

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save_models_link

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text/plain; charset=US-ASCII

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test.m

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train_cifar10.py

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vgg.py

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vgg.py

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vgg_torch.py

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text/x-python