Abstract
The use of the Honeycomb architecture for the implementation of neural networks in the next generation of supercomputers is discussed. The major point is that the relative value of various neural network models depends heavily on the chosen implementation technology. Models that are superior when it comes to conventional implementation architectures and related technologies may be inferior if the Honeycomb architecture and related technologies are used, and vice versa.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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