| Autor: |
Marina Perea-Trigo, Enrique J. López-Ortiz, Jose L. Salazar-González, Juan A. Álvarez-García, J. J. Vegas Olmos |
| Přispěvatelé: |
Universidad de Sevilla. Departamento de Ciencia de la Computación e Inteligencia Artificial, Ministerio de Ciencia e Innovación (MICIN). España, European Commission (EC) |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Electronics; Volume 12; Issue 1; Pages: 146 |
| ISSN: |
2079-9292 |
| Popis: |
The growth of the Internet has led to the emergence of servers that perform increasingly heavy tasks. Some servers must remain active 24 h a day, but the evolution of network cards has facilitated the use of Data Processing Units (DPUs) to reduce network traffic and alleviate server workloads. This capability makes DPUs good candidates for load alleviation in systems that perform continuous data processing when the data can be pre-filtered. Computer vision systems that use some form of artificial intelligence, such as facial recognition or weapon detection, tend to have high workloads and high power consumption, which is becoming increasingly costly. Reducing the workload is therefore desirable and possible in some scenarios. The main contributions of this study are threefold: (1) to explore the potential benefits of using a DPU to alleviate the workload of a 24-h active server; (2) to present a study that measures the workload reduction of a CCTV weapon detection system and evaluate its performance under different conditions. We observed a 43,123% reduction in workload over the 24 h of video used in the experimentation, reaching more than 98% savings during night hours, which significantly reduces system stress and has a direct impact on electrical energy expenditure; and (3) to provide a framework that can be adapted to other computer vision-based detection systems. Ministerio de Ciencia e Innovación PDC2021-121197 Ministerio de Ciencia e Innovación PID2021-126359OB-I00 European Commission ID2PPAC ID:101007254 |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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