Abstract:
:Guided by brain-like 'spiking' computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence-promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm-hardware codesign.
journal_name
Naturejournal_title
Natureauthors
Roy K,Jaiswal A,Panda Pdoi
10.1038/s41586-019-1677-2subject
Has Abstractpub_date
2019-11-01 00:00:00pages
607-617issue
7784eissn
0028-0836issn
1476-4687pii
10.1038/s41586-019-1677-2journal_volume
575pub_type
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