  This paper presents a case for exemplar parallelism of neural networks using
Go as parallelization framework. Further it is shown that also limited
multi-core hardware systems are feasible for these parallelization tasks, as
notebooks and single board computer systems. The main question was how much
speedup can be generated when using concurrent Go goroutines specifically. A
simple concurrent feedforward network for MNIST digit recognition with the
programming language Go was created to find the answer. The first findings when
using a notebook (Lenovo Yoga 2) showed a speedup of 252% when utilizing 4
goroutines. Testing a single board computer (Banana Pi M3) delivered more
convincing results: 320% with 4 goroutines, and 432% with 8 goroutines.
