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Smartphone Crowd Computing: A Rational Approach for Sustainable Computing by Curbing the Environmental Externalities of the Growing Computing Demands
Published in Rik Das, Mahua Banerjee, Sourav De, Emerging Trends in Disruptive Technology Management for Sustainable Development, 2019
Pijush Kanti Dutta Pramanik, Saurabh Pal, Prasenjit Choudhury
Manufacturers like Intel and AMD are putting forward new processor technologies where a single processor can do the job of multiple processors consuming an equal amount of energy. The multi-core processor technology (dual, quad, or octa-core processors) enhances the computing performance significantly by enabling parallel computing capability in a single processor package. The multi-core processor reflects as multiple processors working together with performance much higher than a single processor at lower clock speeds. The voltage consumption per core is less and, thus, typically consumes less power (Silicon Mechanics 2018).
Introduction to Distributed Real-Time Mixed-Criticality Systems
Published in Hamidreza Ahmadian, Roman Obermaisser, Jon Perez, Distributed Real-Time Architecture for Mixed-Criticality Systems, 2018
A promising solution is the integration of electronic functions using fewer ECUs, thereby reducing the mentioned issues due to a high component number and cabling. This integration is in particular facilitated by advances in the semiconductor industry resulting in powerful multi-core processors. A multi-core processor can host and execute several electronic functions in parallel, while meeting stringent temporal requirements.
Distributed and Parallel Computing
Published in Sunilkumar Manvi, Gopal K. Shyam, Cloud Computing, 2021
Sunilkumar Manvi, Gopal K. Shyam
A multi-core processor is a processor that includes multiple processing units (called “cores”) on the same chip. This processor differs from a superscalar processor, which includes multiple execution units and can issue multiple instructions per clock cycle from one instruction stream (thread); in contrast, a multi-core processor can issue multiple instructions per clock cycle from multiple instruction streams.
Multi-view crowd congestion monitoring system based on an ensemble of convolutional neural network classifiers
Published in Journal of Intelligent Transportation Systems, 2020
Yan Li, Majid Sarvi, Kourosh Khoshelham, Milad Haghani
A loading test is conducted to test the maximum number of cameras for which the proposed multi-view congestion classification method can be run in real-time. Because maximum four cameras views can be obtained in our dataset (PETS2009 dataset), in order to solve the problem of limited data sources, the processing time of different cameras is measured and compared. The result shows that the difference of processing time between multiple cameras is less that 0.03 seconds, which is negligible. Also in order to quickly reach the limit of the operating system, our loading test method is to add 10 cameras each time. To gain faster overall execution, multi-threading method is used in our framework, which is to put central processing unit in a multi-core processor to execute multiple processors concurrently within the support of the system.