Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?

Deploying a deep learning model on an edge device means the computation happens locally, on the device itself, rather than on a remote server in the cloud. The most significant benefit of this approach is the reduction in latency. Data does not need to be transmitted over a network to a server for processing and then have the result sent back. This elimination of the network round-trip time is critical for applications requiring real-time responses, such as autonomous vehicles, real-time video analysis, or interactive augmented reality.

A. A more complex model: Edge devices have limited computational power and memory, which restricts the complexity of models that can be deployed, making this a challenge, not a benefit.
B. Guaranteed availability of enough space: Edge devices are resource-constrained, and storage space is typically limited, not guaranteed to be sufficient for any model.
C. Increase in data bandwidth consumption: Edge processing reduces the need to send large amounts of raw data to the cloud, thereby decreasing, not increasing, data bandwidth consumption.

1. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646. https://doi.org/10.1109/JIOT.2016.2579198 (Section II.A, "Advantages of Edge Computing," lists low latency as a primary benefit).

2. Chen, J., & Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8), 1655-1674. https://doi.org/10.1109/JPROC.2019.2921977 (The paper highlights that edge computing can "significantly reduce the service latency" for deep learning applications).

3. Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295-2329. https://doi.org/10.1109/JPROC.2017.2761740 (Discusses how local processing on embedded devices avoids network latency).