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 humanlevel 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.

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Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). One problem that is often tasked to CDOs is asset allocation. Given the number of possible solutions in this allocation problem, determining the optimal solution via CDO can be very time consuming. In this work we show that a grid of isolated spiking neurons is capable of generating solutions to this problem very quickly, although some degree of approximation is required to achieve the speedup. However, the approximate spiking approach presented in this work was able to complete all allocation simulations with greater than 99.9% accuracy. To show the feasibility of low power implementation, this algorithm was executed using the Intel Loihi manycore neuromorphic processor. Given the vast increase in speed (greater than 1000 times in larger allocation problems), as well as the reduction in computational requirements, the presented algorithm is ideal for moving asset allocation to low power, portable, embedded hardware.

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