I have a TFLite model deployed to a Raspberry Pi. I'm using a Coral USB Accelerator to speed up inference, which contains an Edge TPU. I'm interested in experimenting with the impact of using different dataflows on the energy efficiency of this deployment.
Does anyone know how I could specify a particular dataflow, such as row-stationary or output-stationary, when accelerating a TFLite model using an Edge TPU?
For reference: https://people.csail.mit.edu/emer/papers/2017.05.ieee_micro.dnn_dataflow.pdf
According to the Coral support team:
"As far as we know, there is not a specific way available to run the model with a particular data-flow.
It is recommended to visit the model compatibility page at https://coral.ai/docs/edgetpu/models-intro/ to understand how a TFLite model is mapped to the EdgeTPU."