Novel Artificial Intelligence (AI) approaches try to
process an excessive amount of field-level data. However, challenges
arise as network bandwidth is limited, and thus this data
cannot be entirely transferred to the cloud for further processing.
Edge computing tries to overcome that limitation by bringing the
computational resources closer to the data generating sources.
However, edge devices are also constraint by both CPU power and
memory, and strict real-time requirements of the manufacturing
domain have to be met. Thus, the selection of suitable devices
for specific AI algorithms poses a severe challenge. Currently,
the choice is often made by a trial-and-error approach or by
selecting more powerful devices than needed. This paper tries
to address those challenges by showing relevant aspects for
algorithm benchmarking in the manufacturing domain. Selected
algorithms, namely Grubbs Test, Butterworth Filter, DBSCAN,
Random Forest, Support Vector Machine, Matrix Multiplication,
and Matrix Inversion, are examined. Analysis of their theoretical
time and space complexity sheds some light on the behaviour
of the algorithms with respect to their input data points. In
addition, relevant metrics for the manufacturing domain, such as
execution time, memory consumption, and energy consumption,
are identified. This paper furthermore examines the algorithm
behaviour on various heterogeneous hardware devices, such as
PLCs, an MCU, IPCs, a single-board computer, and a dedicated
edge device. Altogether, this paper can guide selecting suitable
algorithms and hardware to equip CPPS with novel data processing
solutions thoughtfully. Moreover, the presented metrics
can support the creation of novel ML benchmarks for smart
manufacturing (SM).
«
Novel Artificial Intelligence (AI) approaches try to
process an excessive amount of field-level data. However, challenges
arise as network bandwidth is limited, and thus this data
cannot be entirely transferred to the cloud for further processing.
Edge computing tries to overcome that limitation by bringing the
computational resources closer to the data generating sources.
However, edge devices are also constraint by both CPU power and
memory, and strict real-time requirements of the manufacturi...
»
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