Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence
In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a lightweight device such as a mobile phone or edge device, and the remaining portion of the DNN is processed where more computing resources are available, such as in the cloud. This paper presents a novel...
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Veröffentlicht in: | IEEE open journal of circuits and systems 2021, Vol.2, p.350-362 |
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description | In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a lightweight device such as a mobile phone or edge device, and the remaining portion of the DNN is processed where more computing resources are available, such as in the cloud. This paper presents a novel lightweight compression technique designed specifically to quantize and compress the features output by the intermediate layer of a split DNN, without requiring any retraining of the network weights. Mathematical models for estimating the clipping and quantization error of leaky-ReLU and ReLU activations at this intermediate layer are used to compute optimal clipping ranges for coarse quantization. A mathematical model for estimating the clipping and quantization error of leaky-ReLU activations at this intermediate layer is developed and used to compute optimal clipping ranges for coarse quantization. We also present a modified entropy-constrained design algorithm for quantizing clipped activations. When applied to popular object-detection and classification DNNs, we were able to compress the 32-bit floating point intermediate activations down to 0.6 to 0.8 bits, while keeping the loss in accuracy to less than 1%. When compared to HEVC, we found that the lightweight codec consistently provided better inference accuracy, by up to 1.3%. The performance and simplicity of this lightweight compression technique makes it an attractive option for coding an intermediate layer of a split neural network for edge/cloud applications. |
doi_str_mv | 10.1109/OJCAS.2021.3072884 |
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This paper presents a novel lightweight compression technique designed specifically to quantize and compress the features output by the intermediate layer of a split DNN, without requiring any retraining of the network weights. Mathematical models for estimating the clipping and quantization error of leaky-ReLU and ReLU activations at this intermediate layer are used to compute optimal clipping ranges for coarse quantization. A mathematical model for estimating the clipping and quantization error of leaky-ReLU activations at this intermediate layer is developed and used to compute optimal clipping ranges for coarse quantization. We also present a modified entropy-constrained design algorithm for quantizing clipped activations. When applied to popular object-detection and classification DNNs, we were able to compress the 32-bit floating point intermediate activations down to 0.6 to 0.8 bits, while keeping the loss in accuracy to less than 1%. When compared to HEVC, we found that the lightweight codec consistently provided better inference accuracy, by up to 1.3%. 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(IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-96427f64c8fb32db1d28bb3d252ff940b4d2a9f3e1559d71706f072634ac58773</citedby><cites>FETCH-LOGICAL-c405t-96427f64c8fb32db1d28bb3d252ff940b4d2a9f3e1559d71706f072634ac58773</cites><orcidid>0000-0003-3154-5743 ; 0000-0001-7724-8993</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9430648$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Cohen, Robert A.</creatorcontrib><creatorcontrib>Choi, Hyomin</creatorcontrib><creatorcontrib>Bajic, Ivan V.</creatorcontrib><title>Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence</title><title>IEEE open journal of circuits and systems</title><addtitle>OJCAS</addtitle><description>In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a lightweight device such as a mobile phone or edge device, and the remaining portion of the DNN is processed where more computing resources are available, such as in the cloud. This paper presents a novel lightweight compression technique designed specifically to quantize and compress the features output by the intermediate layer of a split DNN, without requiring any retraining of the network weights. Mathematical models for estimating the clipping and quantization error of leaky-ReLU and ReLU activations at this intermediate layer are used to compute optimal clipping ranges for coarse quantization. A mathematical model for estimating the clipping and quantization error of leaky-ReLU activations at this intermediate layer is developed and used to compute optimal clipping ranges for coarse quantization. We also present a modified entropy-constrained design algorithm for quantizing clipped activations. When applied to popular object-detection and classification DNNs, we were able to compress the 32-bit floating point intermediate activations down to 0.6 to 0.8 bits, while keeping the loss in accuracy to less than 1%. When compared to HEVC, we found that the lightweight codec consistently provided better inference accuracy, by up to 1.3%. The performance and simplicity of this lightweight compression technique makes it an attractive option for coding an intermediate layer of a split neural network for edge/cloud applications.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Cloud computing</subject><subject>Codec</subject><subject>Collaboration</subject><subject>Collaborative intelligence</subject><subject>deep learning</subject><subject>Design modifications</subject><subject>Entropy coding</subject><subject>Estimation</subject><subject>feature compression</subject><subject>Floating point arithmetic</subject><subject>Image coding</subject><subject>Intelligence</subject><subject>Lightweight</subject><subject>Mathematical analysis</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>Measurement</subject><subject>Mobile handsets</subject><subject>neural network compression</subject><subject>Neural networks</subject><subject>quantization</subject><subject>Quantization (signal)</subject><subject>Tensors</subject><issn>2644-1225</issn><issn>2644-1225</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkc1OAyEUhSdGExvtC-hmEtetcIEZZtk0VmsaXahbCQyXSp2WylCNb-_0J40bDiHnO_eSk2VXlAwpJdXt8-N49DIEAnTISAlS8pOsBwXnAwogTv_dz7N-2y4IISAopVD2sveZn3-kH9ye-Tgs1xHb1odVHlw-XSWMS7ReJ8yfcBN100n6CfEzn6BOm86buxA7rmm0CVEn_407rGn8HFc1XmZnTjct9g96kb1N7l7HD4PZ8_10PJoNak5EGlQFh9IVvJbOMLCGWpDGMAsCnKs4MdyCrhxDKkRlS1qSwnU_LRjXtZBlyS6y6T7XBr1Q6-iXOv6qoL3aPYQ4VzomXzeoiDGaW-oqoQW3VlYoJThpubHUloR1WTf7rHUMXxtsk1qETVx16ysQjIoCKCOdC_auOoa2jeiOUylR21rUrha1rUUdaumg6z3kEfEIVJyRgkv2B7WciQc</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Cohen, Robert A.</creator><creator>Choi, Hyomin</creator><creator>Bajic, Ivan V.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3154-5743</orcidid><orcidid>https://orcid.org/0000-0001-7724-8993</orcidid></search><sort><creationdate>2021</creationdate><title>Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence</title><author>Cohen, Robert A. ; Choi, Hyomin ; Bajic, Ivan V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-96427f64c8fb32db1d28bb3d252ff940b4d2a9f3e1559d71706f072634ac58773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Cloud computing</topic><topic>Codec</topic><topic>Collaboration</topic><topic>Collaborative intelligence</topic><topic>deep learning</topic><topic>Design modifications</topic><topic>Entropy coding</topic><topic>Estimation</topic><topic>feature compression</topic><topic>Floating point arithmetic</topic><topic>Image coding</topic><topic>Intelligence</topic><topic>Lightweight</topic><topic>Mathematical analysis</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>Measurement</topic><topic>Mobile handsets</topic><topic>neural network compression</topic><topic>Neural networks</topic><topic>quantization</topic><topic>Quantization (signal)</topic><topic>Tensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cohen, Robert A.</creatorcontrib><creatorcontrib>Choi, Hyomin</creatorcontrib><creatorcontrib>Bajic, Ivan V.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE open journal of circuits and systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cohen, Robert A.</au><au>Choi, Hyomin</au><au>Bajic, Ivan V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence</atitle><jtitle>IEEE open journal of circuits and systems</jtitle><stitle>OJCAS</stitle><date>2021</date><risdate>2021</risdate><volume>2</volume><spage>350</spage><epage>362</epage><pages>350-362</pages><issn>2644-1225</issn><eissn>2644-1225</eissn><coden>IOJCC3</coden><abstract>In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a lightweight device such as a mobile phone or edge device, and the remaining portion of the DNN is processed where more computing resources are available, such as in the cloud. This paper presents a novel lightweight compression technique designed specifically to quantize and compress the features output by the intermediate layer of a split DNN, without requiring any retraining of the network weights. Mathematical models for estimating the clipping and quantization error of leaky-ReLU and ReLU activations at this intermediate layer are used to compute optimal clipping ranges for coarse quantization. A mathematical model for estimating the clipping and quantization error of leaky-ReLU activations at this intermediate layer is developed and used to compute optimal clipping ranges for coarse quantization. We also present a modified entropy-constrained design algorithm for quantizing clipped activations. When applied to popular object-detection and classification DNNs, we were able to compress the 32-bit floating point intermediate activations down to 0.6 to 0.8 bits, while keeping the loss in accuracy to less than 1%. 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subjects | Accuracy Algorithms Artificial neural networks Cloud computing Codec Collaboration Collaborative intelligence deep learning Design modifications Entropy coding Estimation feature compression Floating point arithmetic Image coding Intelligence Lightweight Mathematical analysis Mathematical model Mathematical models Measurement Mobile handsets neural network compression Neural networks quantization Quantization (signal) Tensors |
title | Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence |
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