Many application areas require resource-efficient computations, for example, computer vision tasks in production. Artificial Intelligence solutions that allow to adapt online to changing conditions in such tasks are promising, but often involve large models and costly computations. This project aims at exploring in how far biological-inspired spiking neural network architectures can be employed in hardware which, on the one hand, allows for efficient computation, and, on the other hand, facilitates online adaption and learning.
Implementing online learning methods into resource efficient hardware will allow to embed such methods directly on sensor hardware, reducing high-bandwidth communications and allow for faster processing. The project will use current embedded (and many core) hardware architectures for testing online learning methods directly in hardware and applied in spiking neural networks. As one example, these systems will be employed for ultra-high-speed computer vision and event detection.
The project will explore the design-space of possible embedded hardware architectures and analyze resource-efficient implementations of biologically-inspired spiking neural networks. The different architectures will be applied in Artificial Intelligence tasks as, for example, in online learning in computer vision. First, with respect to hardware architectures this will cover reconfigurable hardware platforms as FPGA and application specific many-core systems. Secondly, different neuron and synapse models – as well as configurations of these model – will be evaluated, for example variations of spike-timing dependent plasticity. The hardware platforms will finally be coupled with event-based sensors (e.g. DVS cameras) to evaluate the solutions on the basis of practical application scenarios.
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