Electronic design automation methodologies for digital VLSI circuit reliability analysis and optimisation
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Date
2020-08-17
Authors
Yang, Bo
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Publisher
University College Cork
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Abstract
As the continuous scaling on both size and the operating voltage of the Very
Large Scale Integrated (VLSI) circuits for improved power and area efficiencies,
keeping the acceptable reliability has become an increasingly significant challenge
of the digital circuits in addition to the power consumption and area.
In this thesis, a number of modern Electronic Design Automation (EDA) algorithms
and approaches emphasizing the digital circuit reliability analysis and
optimisation were investigated. In this work, the reliability is categorized into
two aspects, i.e., the reliability related to the soft error events in the circuit and
the timing related reliability. In terms of the soft error events, an error may be
injected into the circuit unexpectedly, which may eventually impact the correctness
of the computation executed by the circuit. What makes the analysis complex
is that when an error occurred somewhere in the circuit, the error may or
may not propagate through the circuit and be reflected on the output due to the
masking phenomena. Monte-Carlo (MC) simulation was the standard method to
solve the problem in the traditional workflow until the circuit scale became sufficiently large that it is out of the tractable computation performance. Thus, analytical approaches for soft error propagation algorithms were under research
for decades. In this work, a conditional probability based soft error propagation
algorithm, CPEP, that can achieve significant performance boost compared
to the MC simulation, while maintaining high accuracy was developed. In addition,
building on the foundation of the CPEP reliability analysis algorithm, a
complete EDA framework to enhance the reliability during the circuit synthesis
was proposed.
The timing related reliability is a measure of the probabilities that the circuit
outputs can properly switch to the desired voltage level at a certain delay (i.e.,
Cut-off delay). The analysis of this kind of reliability is a process of Statistical
Static Timing Analysis (SSTA), which has become a very active research area
in the last decades. In our work, the Artificial Neural Network (ANN) function
approximator based SSTA gate models for accurate and fast estimation of the propagation delay, Tpd, distribution (hence the reliability) was explored. All
these methods for reliability analysis are fully compatible with existing electronics
design flows using classical Boolean circuit synthesis methods.
In the quest to design reliable, efficient circuits, alternative circuit architectures
were investigated, which may not follow the classical CMOS design flow.
ANNs are such architectures that promise more efficient implementations of
digital circuits and can be a viable alternative to the current CMOS-based VLSI
circuit architectures. A synthesis framework for Multilayer Perceptron (MLP)
based Boolean logic gates was investigated. Experimental results show that
with this architecture, the logic circuit can be implemented effectively, resulting
in smaller propagation delay and also the more predictable variations. A
typical Boolean circuit consists of many types of gates. Implementing the linear
circuits (i.e., XOR networks) is particularly difficult for an MLP Boolean function
approximator. Thus, a circuit linearization framework based on Boolean
function Bi-decomposition was proposed to separate the linear part from the
non-linear part (composed of the rest of gate types). Then, an MLP-based circuit
can be used to implement the non-linear part more effectively while the
linear part can be part of an error control coding scheme, which can further
improve circuit reliability.
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Keywords
Very large scale integrated circuit (VLSI) , Electronic design automation (EDA) , Digital reliability and optimisation , Statistical static timing analysis (SSTA) , Soft error propagation , MLP based logic circuit
Citation
Yang, B. 2020. Electronic design automation methodologies for digital VLSI circuit reliability analysis and optimisation. PhD Thesis, University College Cork.