Open AccessA Roadmap for Reaching the Potential of Brain‐Derived Computing
Author(s) -
Aimone James B.
Publication year - 2021
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202000191
Subject(s) - neuromorphic engineering , computer science , computer architecture , technology roadmap , cmos , realization (probability) , field (mathematics) , key (lock) , deep learning , scaling , artificial neural network , computer engineering , artificial intelligence , data science , electronic engineering , engineering , computer security , business , mathematics , marketing , pure mathematics , statistics , geometry
Neuromorphic computing is a critical future technology for the computing industry, but it has yet to achieve its promise and has struggled to establish a cohesive research community. A large part of the challenge is that full realization of the potential of brain inspiration requires advances in both device hardware, computing architectures, and algorithms. This simultaneous development across technology scales is unprecedented in the computing field. This article presents a strategy, framed by market and policy pressures, for moving past these current technological and cultural hurdles to realize its full impact across technology. Achieving the full potential of brain‐derived algorithms as well as post‐complementary metal–oxide‐semiconductor (CMOS) scaling neuromorphic hardware requires appropriately balancing the near‐term opportunities of deep learning applications with the long‐term potential of less understood opportunities in neural computing.