The use of heterogeneous MPSoCs (Multi-Processor System-on-Chip) has become increasingly popular in recent years due to their ability to provide high performance and low power consumption. However, one of the challenges associated with using these systems is the implementation of dynamic neural networks (DNNs). DNNs are complex algorithms that require a large amount of computing power and memory resources, making them difficult to implement on heterogeneous MPSoCs.
In order to address this challenge, researchers have proposed an energy-efficient execution scheme for implementing DNNs on heterogeneous MPSoCs. This scheme utilizes a combination of hardware accelerators and software-based techniques to reduce the power consumption of the system while still providing the required performance. The hardware accelerators are used to offload some of the computationally intensive operations from the main processor, while the software-based techniques are used to optimize the execution of the DNNs.
The energy-efficient execution scheme has been evaluated in several studies. In one study, the scheme was used to implement a convolutional neural network (CNN) on a heterogeneous MPSoC. The results showed that the energy-efficient execution scheme was able to reduce the power consumption of the system by up to 40%, while still providing the required performance.
In another study, the energy-efficient execution scheme was used to implement a recurrent neural network (RNN) on a heterogeneous MPSoC. The results showed that the energy-efficient execution scheme was able to reduce the power consumption of the system by up to 50%, while still providing the required performance.
Overall, these studies demonstrate that the energy-efficient execution scheme can be used to effectively implement DNNs on heterogeneous MPSoCs. The scheme reduces the power consumption of the system while still providing the required performance, making it an attractive option for those looking to implement DNNs on heterogeneous MPSoCs.
Source: Plato Data Intelligence: PlatoAiStream
