14:25‑14:35
(10+5 min) | | Lightning talk: Cognitive Domain Ontologies: HPCs to Ultra Low Power Neuromorphic Platforms
Tarek Taha, Chris Yakopcic, Nayim Rahman, Tanvir Atahary and Scott Douglass
The Cognitively Enhanced Complex Event Processing (CECEP) agent-based decision-making architecture is being developed at AFRL/RHCI [1]. Within this agent, the Cognitive Domain Ontology (CDO) component is the slowest for most applications of the agent. We show that even after acceleration on
a high performance server based computing system enhanced with a high end graphics processing unit (GPU), the CDO component does not scale well for real time use on large problem sizes. Thus, to enable real time use of the agent, particularly in power constrained environments (such as autonomous air vehicles), alternative implementations of the agent logic are needed. These alternative implementations need to utilize different algorithms that
implement the CDO logic and need to be targeted to much lower power (and weight) computing systems than GPU enabled servers (which can consume over 500W and weigh over 50lbs).
The objective of this work was to carry out an initial design space search of algorithms and hardware for decision making through the domain knowledge component of CECEP (the CDO) [2-5]. Several algorithmic and circuit approaches are proposed that span across six hardware options of varying power consumption and weight (ranging from over 1000W to less than 1W). The algorithms range from exact solution producers optimized for running on a cluster of high performance computing systems [1] to approximate solution producers running fast on low power neuromorphic hardware [6-9]. The loss in accuracy for the approximate approaches is minimal, making them well suited to SWaP constrained systems, such as UAVs. The exact solution approach on an HPC will give confidence that the best answer has been evaluated (although this may take some time to generate).
[1] T. Atahary, T. Taha, F. Webber, and S. Douglass, “Knowledge mining for cognitive agents through path based forward checking,” 16th IEEE/ACIS SNPD,
pp. 1-8, June, 2015.
[2] C. Yakopcic, N. Rahman, T. Atahary, T. M. Taha, and S. Douglass, “Cognitive Domain Ontologies in a Memristor Crossbar Architecture,” IEEE National
Aerospace and Electronics Conference (NAECON), pp. 76-83, Dayton, OH, June 2017.
[3] N. Rahman, T. Atahary, T. Taha, S. Douglass, "A pattern matching approach to map cognitive domain ontologies to the IBM TrueNorth Neurosynaptic System." 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA). IEEE, 2017.
[4] N. Rahman, C. Yakopcic, T. Atahary, R. Hasan, T. M. Taha, and S. Douglass, “Cognitive Domain Ontologies in Lookup Tables Stored in a Memristor String Matching Architecture,” 30th annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4, Windsor, Ontario, April 2017.
[5] N. Rahman, T. Atahary, C. Yakopcic, T. M. Taha, Scott Douglass, “Task Allocation Performance Comparison for Low Power Devices,” IEEE National
Aerospace and Electronics Conference (NAECON), pp. 247-253, Dayton, OH, July, 2018.
[6] C. Yakopcic, T. Atahary, T. M. Taha, A. Beigh, and S. Douglass, “High Speed Approximate Cognitive Domain Ontologies for Asset Allocation based on
Isolated Spiking Neurons,” IEEE National Aerospace and Electronics Conference (NAECON), pp. 241-246, Dayton, OH, July, 2018.
[7] C. Yakopcic, N. Rahman, T. Atahary, T. M. Taha, A. Beigh, and S. Douglass, “High Speed Approximate Cognitive Domain Ontologies for Constrained Asset Allocation based on Spiking Neurons,” IEEE National Aerospace and Electronics Conference (NAECON), 2019.
[8] 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 (IJCNN), Budapest, Hungary, July, 2019.
[9] C. Yakopcic, J. Freeman, T. M. Taha, S. Douglass, and Q. Wu, “Cognitive Domain Ontologies Based on Loihi Spiking Neurons Implemented Using a Confabulation Inspired Network,” IEEE Cognitive Communications for Aerospace Applications Workshop, June, 2019.
| Tarek Taha et al. |