On the Use of ZBDDs for Implicit and Compact Critical Path Delay Fault Test Generation

2008 ◽  
Vol 24 (1-3) ◽  
pp. 203-222 ◽  
Author(s):  
Kyriakos Christou ◽  
Maria K. Michael ◽  
Spyros Tragoudas
Keyword(s):  
Test Generation ◽  
Critical Path ◽  
Path Delay ◽  
Delay Fault ◽  
Critical Path Delay ◽  
Path Delay Fault

A new classification of path-delay fault testability in terms of stuck-at faults

2004 ◽  
Vol 19 (6) ◽  
pp. 955-964 ◽  
Author(s):  
Subhashis Majumder ◽  
Bhargab B. Bhattacharya ◽  
Vishwani D. Agrawal ◽  
Michael L. Bushnell
Keyword(s):  
Path Delay ◽  
New Classification ◽  
Delay Fault ◽  
Path Delay Fault

scholarly journals A design methodology for approximate multipliers in convolutional neural networks: A case of MNIST

2021 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Kenta Shirane ◽  
Takahiro Yamamoto ◽  
Hiroyuki Tomiyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Design Methodology ◽  
Critical Path ◽  
High Accuracy ◽  
Path Delay ◽  
Trade Off ◽  
Critical Path Delay

In this paper, we present a case study on approximate multipliers for MNIST Convolutional Neural Network (CNN). We apply approximate multipliers with different bit-width to the convolution layer in MNIST CNN, evaluate the accuracy of MNIST classification, and analyze the trade-off between approximate multiplier’s area, critical path delay and the accuracy. Based on the results of the evaluation and analysis, we propose a design methodology for approximate multipliers. The approximate multipliers consist of some partial products, which are carefully selected according to the CNN input. With this methodology, we further reduce the area and the delay of the multipliers with keeping high accuracy of the MNIST classification.

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