Abstract:
This dissertation presents a detailed investigation on the use of approximate
arithmetic circuits in di erent adaptive algorithms. The analysis is rst carried
out by using the traditional work
ow, which is to directly implement the
functional model, i.e. the truth table of these approximate arithmetic blocks.
A faster yet accurate framework is proposed, which analytically model the
estimation error of di erent adaptive algorithms when approximate arithmetic
circuits are employed. The novel framework is veri ed by comparing it
with the functional model, i.e. the truth table based implementation of approximate
LMS. The performance of both implementations are assessed for
various approximation scenarios and our results show that the novel framework
can model the approximate arithmetic based adaptive algorithms with
an accuracy of up to 92%. Furthermore, the computation time is signi -
cantly reduced, i.e. the proposed model is 4000 times faster as compared to
the functional model-based implementation of approximate LMS. Since approximation
introduces error, further analysis is carried out to mitigate the
approximation error by proposing two estimators based on the maximum
likelihood estimation (MLE) and maximum a posteriori estimation (MAP).
These estimators perform signi cantly better than the traditional modeling
of adaptive algorithms using approximate hardware. We also analyzed the
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