Efficient two-dimensional systolic array architecture for multilayer neural network

1997 ◽  
Vol 33 (24) ◽  
pp. 2055 ◽  
Author(s):  
H. Amin ◽  
K.M. Curtis ◽  
B.R. Hayes-Gill
1994 ◽  
Vol 20 (3) ◽  
pp. 233-241
Author(s):  
Mansour H. Jaragh ◽  
Malek G.M. Hussain

1987 ◽  
Vol 12 (11) ◽  
pp. 959 ◽  
Author(s):  
M. Shabeer ◽  
I. Andonovic ◽  
B. Culshaw

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 338
Author(s):  
Keewon Cho ◽  
Ingeol Lee ◽  
Hyeonchan Lim ◽  
Sungho Kang

Neural-network computing has revolutionized the field of machine learning. The systolic-array architecture is a widely used architecture for neural-network computing acceleration that was adopted by Google in its Tensor Processing Unit (TPU). To ensure the correct operation of the neural network, the reliability of the systolic-array architecture should be guaranteed. This paper proposes an efficient systolic-array redundancy architecture that is based on systolic-array partitioning and rearranging connections of the systolic-array elements. The proposed architecture allows both offline and online repair with an extended redundancy architecture and programmable fuses and can ensure reliability even in an online situation, for which the previous fault-tolerant schemes have not been considered.


1992 ◽  
Vol 139 (2) ◽  
pp. 147
Author(s):  
G.A. Orton ◽  
L.E. Peppard ◽  
S.G. Akl

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