The world of technology is constantly evolving and advancing, and one of the most exciting developments in recent years has been the emergence of heterogeneous multi-processor system-on-chips (MPSoCs). These systems are capable of combining multiple processing elements, such as CPUs, GPUs, and DSPs, to provide a powerful and energy-efficient platform for a variety of applications. One such application is the use of MPSoCs for dynamic neural networks (DNNs). DNNs are a type of artificial intelligence that can be used to solve complex problems and make decisions.
However, running DNNs on MPSoCs presents a number of challenges. One of these challenges is the need for an energy-efficient execution scheme that can take advantage of the heterogeneous nature of the system. To address this challenge, researchers have developed a number of different energy-efficient execution schemes for DNNs on MPSoCs.
In this article, we will discuss one such energy-efficient execution scheme for DNNs on MPSoCs. This scheme is based on a technique called “dynamic task scheduling” (DTS). The idea behind DTS is to dynamically assign tasks to different processing elements in order to maximize energy efficiency. To do this, the system first identifies the most energy-efficient way to execute a given task. It then assigns the task to the appropriate processing element and schedules it for execution.
The proposed DTS scheme has been evaluated on a number of different MPSoCs. The results show that it can significantly reduce the energy consumption of DNNs on these systems. In one study, the proposed scheme was able to reduce the energy consumption of a DNN by up to 40%. This is an impressive result, as it demonstrates that the proposed scheme can effectively take advantage of the heterogeneous nature of MPSoCs to achieve significant energy savings.
Overall, this study provides valuable insight into how energy-efficient execution schemes can be used to run DNNs on MPSoCs. By taking advantage of the heterogeneous nature of these systems, it is possible to reduce energy consumption while still achieving high performance. This is an important step towards making MPSoCs a viable platform for running DNNs in the future.
Source: Plato Data Intelligence: PlatoAiStream