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Published in 2022 at "Cluster Computing"
DOI: 10.1007/s10586-021-03494-y
Abstract: Convolutions are the core operation of deep learning applications based on Convolutional Neural Networks (CNNs). Current GPU architectures are highly efficient for training and deploying deep CNNs, and are largely used in production. State–of–the–art implementations,…
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Keywords:
implementation;
implementation convolution;
cnn inference;
convolution ... See more keywords
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2
Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3176408
Abstract: Convolutional neural networks (CNNS) enable machines to view the world as humans and become increasing prevalent for Internet of Things (IoT) applications. Instead of streaming the raw data to the cloud and executing CNN inference…
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Keywords:
distributed situ;
inference;
internet things;
situ cnn ... See more keywords
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3
Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2023.3237572
Abstract: For model inference of convolutional neural networks (CNNs), we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute- and memory-intensive CNNs on Internet of Things devices at the…
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Keywords:
inference;
edge devices;
exploration;
distributed cnn ... See more keywords
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Published in 2021 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2021.3095825
Abstract: Parameter quantization with lower bit-width is the common approach to reduce the computation loads in CNN inference. With the parameters being replaced by fixed-width binaries, multiplication operations can be replaced by the look-up-table (LUT), where…
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Keywords:
table lut;
cnn inference;
computation;
multiplication ... See more keywords