I am trying to implement Tensorflow OpKernels on Raspberry Pi3 GPU (QPU) for operations like Conv2D,Pooling,ReLU etc. The operations are mainly targeted to improve performance during inference and do not care about training (hence back propagation and gradients).
Is using XLA a right approach to achieve this or is there any better way to do?