Data and Power Efficient Intelligence with Neuromorphic Learning Machines

Emre O. Neftci
2018 iScience  
The success of deep networks and recent industry involvement in brain-inspired computing is igniting a widespread interest in neuromorphic hardware that emulates the biological processes of the brain on an electronic substrate. This review explores interdisciplinary approaches anchored in machine learning theory that enable the applicability of neuromorphic technologies to real-world, humancentric tasks. We find that (1) recent work in binary deep networks and approximate gradient descent
more » ... ng are strikingly compatible with a neuromorphic substrate; (2) where real-time adaptability and autonomy are necessary, neuromorphic technologies can achieve significant advantages over mainstream ones; and (3) challenges in memory technologies, compounded by a tradition of bottom-up approaches in the field, block the road to major breakthroughs. We suggest that a neuromorphic learning framework, tuned specifically for the spatial and temporal constraints of the neuromorphic substrate, will help guiding hardware algorithm co-design and deploying neuromorphic hardware for proactive learning of real-world data.
doi:10.1016/j.isci.2018.06.010 pmid:30240646 pmcid:PMC6123858 fatcat:zo4dvtgo75c7pn6n7tal24fkly