Attention leads the gaze of the observer towards interesting items, allowing a detailed analysis only for selected regions of a scene. A robot can take advantage of the perceptual organisation of the features in the scene to guide its attention to better understand its environment. Current bottom–up attention models work with standard RGB cameras requiring a significant amount of time to detect the most salient item in a frame-based fashion. Event-driven cameras are an innovative technology to asynchronously detect contrast changes in the scene with a high temporal resolution and low latency. We propose a new neuromorphic pipeline exploiting the asynchronous output of the event-driven cameras to generate saliency maps of the scene. In an attempt to further decrease the latency, the neuromorphic attention model is implemented in a spiking neural network on SpiNNaker, a dedicated neuromorphic platform. The proposed implementation has been compared with its bio-inspired GPU counterpart, and it has been benchmarked against ground truth fixational maps. The system successfully detects items in the scene, producing saliency maps comparable with the GPU implementation. The asynchronous pipeline achieves an average of 16 ms latency to produce a usable saliency map.

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