Methods of Statistical Analysis and Machine Learning for the Evaluation of Generated Hardware and Firmware Design
Sprache des Titels:
Englisch
Original Buchtitel:
International Workshop on Combinations of Intelligent Methods and Applications
Original Kurzfassung:
In order to continuously increase design productivity, engineers
and researchers rely on automation frameworks for hardware
and firmware design purposes. This does not only guarantee an easier
implementation of components, but also create 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 Statistical 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 efficiency and to avoid
manual effort during the retrieval. To point out this concept, we implement
the generation of data for the estimation of the Hardware Area
and Firmware Metrics 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. Furthermore, ranking
of design features of RI component will be analyzed in accordance with
their importance to the estimation of Hardware Area and Firmware Metrics.
Forschungs-