  In the last decade, special purpose computing systems, such as Neuromorphic
computing, have become very popular in the field of computer vision and machine
learning for classification tasks. In 2015, IBM's released the TrueNorth
Neuromorphic system, kick-starting a new era of Neuromorphic computing.
Alternatively, Deep Learning approaches such as Deep Convolutional Neural
Networks (DCNN) show almost human-level accuracies for detection and
classification tasks. IBM's 2016 release of a deep learning framework for
DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn). Eedn shows
promise for delivering high accuracies across a number of different benchmarks,
while consuming very low power, using IBM's TrueNorth chip. However, there are
many things that remained undiscovered using the Eedn framework for
classification tasks on a Neuromorphic system. In this paper, we have
empirically evaluated the performance of different DCNN architectures
implemented within the Eedn framework. The goal of this work was discover the
most efficient way to implement DCNN models for object classification tasks
using the TrueNorth system. We performed our experiments using benchmark data
sets such as MNIST, COIL 20, and COIL 100. The experimental results show very
promising classification accuracies with very low power consumption on IBM's
NS1e Neurosynaptic system. The results show that for datasets with large
numbers of classes, wider networks perform better when compared to deep
networks comprised of nearly the same core complexity on IBM's TrueNorth
system.
