A Roadmap for Reaching the Potential of Brain-Derived Computing
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1760460
- Report Number(s):
- SAND--2020-11106J; {691445,"Journal ID: ISSN 2640-4567"}
- Journal Information:
- Advanced Intelligent Systems, Journal Name: Advanced Intelligent Systems Journal Issue: 1 Vol. 3; ISSN 2640-4567
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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