  The experience shows that cooperating and communicating computing systems,
comprising segregated single processors, have severe performance limitations.
In his classic "First Draft" von Neumann warned that using a "too fast
processor" vitiates his simple "procedure" (but not his computing model!);
furthermore, that using the classic computing paradigm for imitating neuronal
operations, is unsound. Amdahl added that large machines, comprising many
processors, have an inherent disadvantage. Given that ANN's components are
heavily communicating with each other, they are built from a large number of
components designed/fabricated for use in conventional computing, furthermore
they attempt to mimic biological operation using improper technological
solutions, their achievable payload computing performance is conceptually
modest. The type of workload that AI-based systems generate leads to an
exceptionally low payload computational performance, and their
design/technology limits their size to just above the "toy" level systems: the
scaling of processor-based ANN systems is strongly nonlinear. Given the
proliferation and growing size of ANN systems, we suggest ideas to estimate in
advance the efficiency of the device or application. Through analyzing
published measurements we provide evidence that the role of data transfer time
drastically influences both ANNs performance and feasibility. It is discussed
how some major theoretical limiting factors, ANN's layer structure and their
methods of technical implementation of communication affect their efficiency.
The paper starts from von Neumann's original model, without neglecting the
transfer time apart from processing time; derives an appropriate interpretation
and handling for Amdahl's law. It shows that, in that interpretation, Amdahl's
Law correctly describes ANNs.
