Future Multi-Domain Operations (MDO) will require the coordination of hundreds—even thousands—of devices and component services. This will demand the capability to rapidly discover the distributed devices/services and combine them into different workflow configurations, thereby creating the applications necessary to support changing mission needs. Motivated by neuromorphic processing models, in previous work it was shown that this can be achieved by using hyperdimensional symbolic semantic vector representations of the services/devices and workflows. Using a process of vector exchange the required services are dynamically discovered and inter-connected to achieve the required tasks. In network edge environments, the capability to perform these tasks with minimum energy consumption is critical. This paper describes how emerging spiking neural network (SNN) neuromorphic processing devices can be used to perform the required hyperdimensional vector computation (HDC) with significant energy savings compared to what can be achieved using traditional CMOS implementations.
Future Multi-Domain Operations (MDO) will require the coordination of hundreds, even thousands, of devices and component services. This will demand the capability to rapidly discover the distributed devices/services and combine them into different work ow configurations, thereby creating the applications necessary to support changing mission needs. To meet these objectives, we envision a distributed Cognitive Computing System (CCS) that consists of humans and software that work together as a ‘Distributed Federated Brain'. Motivated by neuromorphic processing models, we present an approach that uses hyper-dimensional symbolic semantic vector representations of the services/devices and workflows. We show how these can be used to perform decentralized service/device discovery and work ow composition in the context of a dynamic communications re-planning scenario. In this paper, we describe how emerging analogue AI ‘In Memory' and ‘Near Memory' computing devices can be used to efficiently perform some of the required hyper-dimensional vector computation (HDC). We present an evaluation of the performance of an energy-efficient phase change memory device (PCM) that can perform the required vector operations and discuss how such devices could be used in energy-critical ‘edge of network' tactical MDO operations.
Machine learning approaches like deep neural networks have proven to be very successful in many domains. However, they require training on a huge volumes of data. While these approaches work very well in a few selected domains where a large corpus of training data exists, they shift the bottleneck in development of machine learning applications to the data acquisition phase and are difficult to use in domains where training data is hard to acquire. For sensor fusion applications in coalition operations, access to good training data that will be suitable for real-life applications is hard to get. The training data sets available are limited in size. For these domains, we need to explore approaches for machine learning which can work with small amounts of data. In this paper, we will look at the current and emerging approaches which allow us to build machine learning models when access to the training data is limited. The approaches examined include statistical machine learning, transfer learning, synthetic data generation, semi-supervised learning and one-shot learning.
Coalition operations of the future will see an increased use of autonomous vehicles, mules and UAVs in different kinds of contexts. Because of the scalability and dynamicity of operations at the tactical edge, such vehicles along with the supporting infrastructure at base-camps and other forward operating bases would need to support an increased degree of autonomy. In this paper, we look at one specific scenario where a surveillance mission needs to be performed sharing resources borrowed from multiple coalition partners. In such an environment, experts who can define security and other types of policies for devices are hard to find. One way to address this problem is to use generative policies – an approach where the devices generate policies for their operations themselves without requiring human involvement as the configuration of the system changes. We show how access control policies can be created automatically by the different devices involved in the mission, with only high-level guidance provided by humans. The generative policy architecture can enable rapid reconfiguration of security policies needed to address dynamic changes from features such as auto-scaling. It can also support improved security in coalition contexts by enabling the solutions to use approaches like moving target defense. In this paper, we would discuss a general architecture which allows the generative policy approach to be used in many different situations, a simulation implementation of the architecture and lessons learnt from the implementation of the simulation.
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