Quality Assessment of Generated Hardware Designs Using Statistical Analysis and Machine Learning
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
8th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2018)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
In order to continuously increase design productivity, engineers
and researchers rely on automation frameworks for hardware design
purposes. This does not only guarantee an easier implementation of
components, but creates a larger margin for improvement by generating
design variants. Within this framework, a major problem for optimizing
the generated design is retrieving data from which a prediction function
(e.g. area, speed, power consumption) could be learned correctly
(since a complete generation, i.e. synthesis of the hardware design, is
too computationally expensive to be performed for a wide set of variants).
In particular, the data used for learning the prediction function
should be representative of valid design possibilities and be generated in
an efficient way. As one contribution, this paper describes how Statis-
tical Analysis (SA) and Machine Learning (ML) are used to guarantee
the quality of the data. At the same time, its retrieval should avoid time
consumption and manual effort. Therefore, this paper also proposes an
automatic approach to generate representative and valid configuration
samples both to improve the effciency and to avoid manual effort during
the retrieval. To point out this concept, we implement the generation of
data for the estimation of the area of a Register Interface (RI) component.
The proposed methods, implemented through SA and ML, allow to
supervise the correctness of the generated data and the learning process
itself. As a consequence, given the correctly generated data, the process
of learning the RI area through a data-driven ML algorithm guarantees
a still accurate (R² = 0:98) but 600x faster estimation.
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