In recent years, the demand for energy-efficient computing has grown exponentially. This is especially true in the field of artificial intelligence, where neural networks are becoming increasingly complex and require more power to operate. To meet this demand, researchers have been exploring ways to optimize the execution of dynamic neural networks on heterogeneous multiprocessor system-on-chips (MPSoCs). This article will explore the current state of research in this area and discuss a study of an energy-efficient execution scheme for dynamic neural networks on MPSoCs.
Dynamic neural networks (DNNs) are a type of artificial neural network that can adapt to changing input data. This makes them ideal for applications such as image recognition, natural language processing, and autonomous driving. However, due to their complexity, they require a large amount of energy to operate. To address this issue, researchers have proposed various energy-efficient execution schemes for DNNs on MPSoCs.
One such scheme is the “heterogeneous mapping” approach. This approach involves mapping different parts of the DNN onto different processing cores on the MPSoC. By doing so, the energy consumption of the system can be reduced since the cores can be used more efficiently. Furthermore, this approach allows for better scalability since the number of cores can be increased or decreased depending on the application’s needs.
Another energy-efficient execution scheme is the “dynamic scheduling” approach. This approach involves scheduling different parts of the DNN onto different cores in a dynamic manner. This allows for better utilization of the cores since they can be used more efficiently. Furthermore, this approach also allows for better scalability since the number of cores can be increased or decreased depending on the application’s needs.
In order to evaluate these approaches, researchers have conducted a study of an energy-efficient execution scheme for dynamic neural networks on MPSoCs. The study compared the two approaches mentioned above and evaluated their performance in terms of energy consumption and scalability. The results showed that both approaches were able to reduce energy consumption and improve scalability when compared to traditional methods. Furthermore, the dynamic scheduling approach was found to be more efficient than the heterogeneous mapping approach in terms of energy consumption and scalability.
Overall, this study has shown that energy-efficient execution schemes for dynamic neural networks on MPSoCs can be achieved through both heterogeneous mapping and dynamic scheduling approaches. These approaches can reduce energy consumption and improve scalability when compared to traditional methods. As such, they are promising solutions for reducing energy consumption in artificial intelligence applications.
Source: Plato Data Intelligence: PlatoAiStream