Spiking neural networks implemented for sensing and control of robots have the potential to achieve lower latency and power consumption by processing information sparsely and asynchronously. They have been used on neuromorphic devices to estimate optical flow for micro air vehicles navigation, however robotic implementations have been limited to hardware setups with sensing and processing as separate systems. This article investigates a new approach for training a spiking neural network for optical flow to be deployed on the speck2e device from Synsense. The method takes into account the restrictions of the speck2e in terms of network architecture, neuron model, and number of synaptic operations and it involves training a recurrent neural network with ReLU activation functions, which is subsequently converted into a spiking network. A system of weight rescaling is applied after conversion, to ensure optimal information flow between the layers. Our study shows that it is possible to estimate optical flow with Integrate-and-Fire neurons. However, currently, the optical flow estimation performance is still hampered by the number of synaptic operations. As a result, the network presented in this work is able to estimate optical flow in a range of [-4, 1] pixel/s.