  Neuromorphic computing mimics the neural activity of the brain through
emulating spiking neural networks. In numerous machine learning tasks,
neuromorphic chips are expected to provide superior solutions in terms of cost
and power efficiency. Here, we explore the application of Loihi, a neuromorphic
computing chip developed by Intel, for the computer vision task of image
retrieval. We evaluated the functionalities and the performance metrics that
are critical in content-based visual search and recommender systems using
deep-learning embeddings. Our results show that the neuromorphic solution is
about 2.5 times more energy-efficient compared with an ARM Cortex-A72 CPU and
12.5 times more energy-efficient compared with NVIDIA T4 GPU for inference by a
lightweight convolutional neural network without batching while maintaining the
same level of matching accuracy. The study validates the potential of
neuromorphic computing in low-power image retrieval, as a complementary
paradigm to the existing von Neumann architectures.
