Low Latency Implementations of CNN for Resource-Constrained IoT Devices
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mujtaba, Ahmed | - |
dc.contributor.author | Lee, Wai-Kong | - |
dc.contributor.author | Hwang, Seong Oun | - |
dc.date.accessioned | 2023-03-14T06:40:32Z | - |
dc.date.available | 2023-03-14T06:40:32Z | - |
dc.date.created | 2023-03-14 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 1549-7747 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87089 | - |
dc.description.abstract | Convolutional Neural Network (CNN) inference on a resource-constrained Internet-of-Things (IoT) device (i.e., ARM Cortex-M microcontroller) requires careful optimization to reduce the timing overhead. We propose two novel techniques to improve the computational efficiency of CNNs by targeting low-cost microcontrollers. Our techniques utilize on-chip memory and minimize redundant operations, yielding low-latency inference results on complex quantized models such as MobileNetV1. On the ImageNet dataset for per-layer quantization, we reduce inference latency and Multiply-and-Accumulate (MAC) per cycle by 22.4% and 22.9%, respectively, compared to the state-of-theart mixed-precision CMix-NN library. On the CIFAR-10 dataset for per-channel quantization, we reduce inference latency and MAC per cycle by 31.7% and 31.3%, respectively. The achieved low-latency inference results can improve the user experience and save power budget in resource-constrained IoT devices. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | - |
dc.title | Low Latency Implementations of CNN for Resource-Constrained IoT Devices | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000922028300100 | - |
dc.identifier.doi | 10.1109/TCSII.2022.3205029 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, v.69, no.12, pp.5124 - 5128 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85137943322 | - |
dc.citation.endPage | 5128 | - |
dc.citation.startPage | 5124 | - |
dc.citation.title | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | - |
dc.citation.volume | 69 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | Mujtaba, Ahmed | - |
dc.contributor.affiliatedAuthor | Lee, Wai-Kong | - |
dc.contributor.affiliatedAuthor | Hwang, Seong Oun | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Internet-of-Things | - |
dc.subject.keywordAuthor | microcontrollers | - |
dc.subject.keywordAuthor | tiny ML | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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