  Parameters of recent neural networks require a huge amount of memory. These
parameters are used by neural networks to perform machine learning tasks when
processing inputs. To speed up inference, we develop Partition Pruning, an
innovative scheme to reduce the parameters used while taking into consideration
parallelization. We evaluated the performance and energy consumption of
parallel inference of partitioned models, which showed a 7.72x speed up of
performance and a 2.73x reduction in the energy used for computing pruned
layers of TinyVGG16 in comparison to running the unpruned model on a single
accelerator. In addition, our method showed a limited reduction some numbers in
accuracy while partitioning fully connected layers.
