Eager Recirculating Memory to Alleviate the Von Neumann Bottleneck

被引:0
|
作者
Edwards, Jonathan [1 ]
O'Keefe, Simon [1 ]
机构
[1] Univ York, York Ctr Complex Syst Anal, York, N Yorkshire, England
来源
PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | 2016年
关键词
NEURAL-NETWORKS;
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
This paper presents an examination of channel based time delays and their application as units which perform storage and computation. We describe the implementation of compound arithmetic operations, and show that by re-circulating the impulses along a channel, both memory and computation can be achieved on the same general channel unit. In addition, this approach has the further advantage of performing arithmetic simplification eagerly, so that the resultant use of memory is optimised by the intermediate processing during memory circulation phases.
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页数:5
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