Neuromorphic Computing for Scientific Applications

被引：2
|
作者：
Patton, Robert [1 ]
Date, Prasanna [1 ]
Kulkarni, Shruti [1 ]
Gunaratne, Chathika [1 ]
Lim, Seung-Hwan [1 ]
Cong, Guojing [1 ]
Young, Steven R. [1 ]
Coletti, Mark [1 ]
Potok, Thomas E. [1 ]
Schuman, Catherine D. [2 ]
机构：
[1] Oak Ridge Natl Lab, Comp Sci & Math Div, Oak Ridge, TN 37830 USA
[2] Univ Tennessee, Dept EECS, Knoxville, TN USA
来源：
2022 IEEE/ACM REDEFINING SCALABILITY FOR DIVERSELY HETEROGENEOUS ARCHITECTURES WORKSHOP (RSDHA) | 2022年
关键词：
neuromorphic computing; spiking neural networks; neural simulation; scientific applications; HARDWARE; DESIGN;
D O I：
10.1109/RSDHA56811.2022.00008
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Neuromorphic computing technology continues to make strides in the development of new algorithms, devices, and materials. In addition, applications have begun to emerge where neuromorphic computing shows promising results. However, numerous barriers to further development and application remain. In this work, we identify several science areas where neuromorphic computing can either make an immediate impact (within 1 to 3 years) or the societal impact would be extremely high if the technological barriers can be addressed. We identify both opportunities and hurdles to the development of neuromorphic computing technology for these areas. Finally, we discuss future directions that need to be addressed to expand both the development and application of neuromorphic computing.
引用
收藏
页码：22 / 28
页数：7
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