Which of the following statements about on-device execution is incorrect?

On-device execution refers to running models directly on hardware devices, such as smartphones or edge devices, instead of relying on a remote server. This approach offers advantages, such as reduced latency and increased privacy. However, it also comes with certain challenges.

The statement regarding the improvement in accelerator usage from model execution is misleading. In many cases, on-device execution may not fully maximize the potential of hardware accelerators due to limitations in device resources, varying levels of computational capability, and the specific nature of the models being deployed. While on-device execution can lead to better responsiveness and lower latency, it does not inherently guarantee improved usage of accelerators compared to scenarios where models are executed on more powerful host systems. This nuanced performance trade-off is an important consideration in the context of AI deployment.

The statements about challenges like the memory wall and high interaction overhead, along with the benefits of MindSpore in enhancing parallelism and reducing synchronization latency, accurately reflect some of the technical issues and optimizations related to on-device AI execution. Thus, the assertion about improved accelerator usage does not align with the complexities and limitations faced in actual on-device execution contexts.