Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments
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| Publicat a: | Electronics vol. 14, no. 12 (2025), p. 2495-2549 |
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| Autor principal: | |
| Altres autors: | , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3223908949 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14122495 |2 doi | |
| 035 | |a 3223908949 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Ngo Dat |u Department of Computer Engineering, Korea National University of Transportation, Chungju 27469, Republic of Korea; datngo@ut.ac.kr (D.N.); hc.park@ut.ac.kr (H.-C.P.) | |
| 245 | 1 | |a Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Deploying deep neural networks (DNNs) in resource-limited environments—such as smartwatches, IoT nodes, and intelligent sensors—poses significant challenges due to constraints in memory, computing power, and energy budgets. This paper presents a comprehensive review of recent advances in accelerating DNN inference on edge platforms, with a focus on model compression, compiler optimizations, and hardware–software co-design. We analyze the trade-offs between latency, energy, and accuracy across various techniques, highlighting practical deployment strategies on real-world devices. In particular, we categorize existing frameworks based on their architectural targets and adaptation mechanisms and discuss open challenges such as runtime adaptability and hardware-aware scheduling. This review aims to guide the development of efficient and scalable edge intelligence solutions. | |
| 653 | |a Deep learning | ||
| 653 | |a Smartphones | ||
| 653 | |a Hardware | ||
| 653 | |a Bandwidths | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Data processing | ||
| 653 | |a Software upgrading | ||
| 653 | |a Co-design | ||
| 653 | |a Privacy | ||
| 653 | |a Energy consumption | ||
| 653 | |a Efficiency | ||
| 653 | |a Energy budget | ||
| 653 | |a Neurons | ||
| 653 | |a Embedded systems | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Edge computing | ||
| 653 | |a Power | ||
| 653 | |a Sensors | ||
| 653 | |a Decision making | ||
| 653 | |a Autonomous vehicles | ||
| 653 | |a Neural networks | ||
| 653 | |a Inference | ||
| 653 | |a Network latency | ||
| 653 | |a Remote computing | ||
| 653 | |a Connectivity | ||
| 653 | |a Intelligence | ||
| 653 | |a Algorithms | ||
| 653 | |a Surveillance | ||
| 700 | 1 | |a Park, Hyun-Cheol |u Department of Computer Engineering, Korea National University of Transportation, Chungju 27469, Republic of Korea; datngo@ut.ac.kr (D.N.); hc.park@ut.ac.kr (H.-C.P.) | |
| 700 | 1 | |a Kang Bongsoon |u Department of Electronics Engineering, Dong-A University, Busan 49315, Republic of Korea | |
| 773 | 0 | |t Electronics |g vol. 14, no. 12 (2025), p. 2495-2549 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223908949/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223908949/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223908949/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |