High Speed Cognitive Domain Ontologies for Asset Allocation Using Loihi Spiking Neurons
| Autor: | Tarek M. Taha, Chris Yakopcic, Tanvir Atahary, Nayim Rahman, Scott Douglass, Alex Beigh |
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| Rok vydání: | 2019 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Decision support system Speedup Computer science Intelligence analysis Distributed computing Complex event processing Computer Science - Neural and Evolutionary Computing 02 engineering and technology Ontology (information science) Grid 020202 computer hardware & architecture Reduction (complexity) 0202 electrical engineering electronic engineering information engineering Ontology FOS: Electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Neural and Evolutionary Computing (cs.NE) Electrical Engineering and Systems Science - Signal Processing |
| Zdroj: | IJCNN |
| DOI: | 10.48550/arxiv.1906.12338 |
| Popis: | 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. Comment: Accepted and to appear in the proceedings of the 2019 IEEE-INNS International Joint Conference on Neural Networks. Citation: C. Yakopcic, T. Atahary, N. Rahman, T. M. Taha, A. Beigh, and S. Douglass, High Speed Approximate Cognitive Domain Ontologies for Asset Allocation Using Loihi Spiking Neurons, IEEE-INNS International Joint Conference on Neural Networks, Budapest, Hungary, July, 2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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