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This paper presents an approach for improving the overall performance of a general purpose application running as a task graph on a many-core neuromorphic supercomputer. Our task graph framework is based on graceful degradation and amelioration paradigms that strive to achieve high reliability and performance by incorporating fault tolerance and task spawning features. The optimization is applied...
Much of recent researches in robotics have shifted the focus from traditionally-specific industrial tasks to investigations of new types of robots with alternative ways of controlling them. In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics. We focused on the kinematics aspect of robot control because it provides a fast and systematic solution...
This paper presents an efficient strategy to implement parallel and distributed computing for image processing on a neuromorphic platform. We use SpiNNaker, a many-core neuromorphic platform inspired by neural connectivity in the brain, to achieve fast response and low power consumption. Our proposed method is based on fault-tolerant finegrained parallelism that uses SpiNNaker resources optimally...
This paper presents a probabilistic graphical model in the form of a factor graph to perform hierarchical probabilistic inference by computing kinematics of an omnidirectional mobile robot. We propose applying population coding principles to encode messages transmitted within the factor graph to update the network's internal belief, as inspired by neuronal information processing. We examine two inference...
When working with probabilistic graphical models we usually have two options to build the model: either using a Bayesian network (BN) or a Markov random field (MRF). However, there exist one more graphical representation which is able to unify the properties of BN and MRF that is called Factor Graph. This paper describes conceptual methods in working with factor graph especially with discrete random...
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