scholarly journals Dynamic range, stability, and fault-tolerant capability of finite-precision RLS systolic array based on Givens rotations

1991 ◽  
Vol 38 (6) ◽  
pp. 625-636 ◽  
Author(s):  
K.R. Liu ◽  
S.-F. Hsieh ◽  
K. Yao ◽  
C.-T. Chiu
Keyword(s):  
Systolic Array ◽  
Dynamic Range ◽  
Fault Tolerant ◽  
Finite Precision ◽  
Givens Rotations

scholarly journals Efficient Systolic-Array Redundancy Architecture for Offline/Online Repair

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 338
Author(s):  
Keewon Cho ◽  
Ingeol Lee ◽  
Hyeonchan Lim ◽  
Sungho Kang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Systolic Array ◽  
Fault Tolerant ◽  
Processing Unit ◽  
Network Computing ◽  
Correct Operation ◽  
Array Architecture ◽  
The Neural Network ◽  
Neural Network Computing

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.

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