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For edge devices that have extremely limited resources such as low-end Internet of Things (IoT) devices, they may still not be able to afford executing the most memory and computation-efficient DNN models locally. In such a scenario, instead of running the DNN models locally, it is necessary to offload the execution of DNN models. As mentioned in the introduction section, offloading to the cloud has a number of drawbacks, including leaking user privacy and suffering from unpredictable end-to-end network latency that could affect user experience, especially when real-time feedback is needed. Considering those drawbacks, a better option is to offload to nearby edge devices that have ample resources to execute the DNN models.

To realize edge offloading, the key is to come up with a model partition and allocation scheme that determines which part of model should be executed locally and which part of model should be offloading. To answer this question, the first aspect that needs to take into account is the size of intermediate results of executing a DNN model. A DNN model adopts a layered architecture. The sizes of intermediate results generated out of each layer have a pyramid shape (Figure 3.3), decreasing from lower layers to higher layers. As a result, partitioning at lower layers would generate larger sizes of intermediate results, which could increase the transmission latency. The second aspect that needs to take into account is the amount of information to be transmitted. For a DNN model, the amount of information generated out of each layer decreases from lower layers to higher layers. Partitioning at lower layers would prevent more information from being transmitted, thus preserving more privacy. As such, the edge offloading scheme creates a trade-off between computation workload, transmission latency, and privacy preservation.

Figure 3.3 Illustration of intermediate results of a DNN model. The size of intermediate results generated out of each layer decreases from lower layers to higher layers. The amount of information generated out of each layer also decreases from lower layers to higher layers.