BrainGrid plus Workbench: High-Performance/High-Quality Neural Simulation

被引:0
|
作者
Stiber, Michael [1 ]
Kawasaki, Fumitaka [1 ]
Davis, Delmar B. [1 ]
Asuncion, Hazeline U. [1 ]
Lee, Jewel Yun-Hsuan [1 ]
Boyer, Destiny [1 ]
机构
[1] Univ Washington, Sch Sci Technol Engn & Math, Comp & Software Syst Div, Bothell, WA 98011 USA
基金
美国国家科学基金会;
关键词
MODEL;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Availability of affordable hardware that in effect enables desktop supercomputing has enabled more ambitious neural simulations driven by more complex software. However, this opportunity comes with costs, in terms of long learning curves to take advantage of the performance possibilities of idiosyncratic, architecturally heterogenous hardware and decreasing ability to be confident in the quality of simulation results. This paper describes a new neural simulation and software/data provenance framework that reduces the difficulty of taking full advantage of GPU computing and increases investigator confidence that simulations results are valid.
引用
收藏
页码:2469 / 2476
页数:8
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