Perspective: Brain-inspired computing needs a master plan
We do not propose that neuromorphic systems will, or should, replace conventional computing platforms. Instead, precision calculations should remain the preserve of digital computation, whereas neuromorphic systems can process unstructured data, perform image recognition, classification of noisy and uncertain datasets, and underpin novel learning and inference systems. In autonomous and IoT-connected systems, they can provide huge energy savings over their conventional counterparts.
Further, we should not neglect the challenges of integrating new neuromorphic technologies at the system level. Beyond the development of brain-inspired devices and algorithms there are pressing questions around how existing, mainstream, AI systems can be replaced with functionally equivalent neuromorphic alternatives. This further emphasizes the need for a fully integrated approach to brain-inspired computation.
The neuromorphic community is a large and growing one, but one that lacks a focus. Although there are numerous conferences, symposia and journals emerging in this space there remains much work to be done to bring the disparate communities together and to corral their efforts to persuade funding bodies and governments of the importance of this field.
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