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Operating Accelerated Neuromorphic Hardware - A Scalable and Sustainable Approach
2021
Accelerated mixed-signal neuromorphic hardware presents a promising approach to overcome run time and scalability issues of software-based neural network simulations. It accomplishes this by physical emulation of the neuronal dynamics via specialized analog circuitry instead of numerical calculations. However, facilitating the advantage of such highly custom hardware with a similar convenience as conventional simulators poses various challenges. This thesis addresses these in two ways: First a
doi:10.11588/heidok.00030979
fatcat:q3gdxru2i5aava3o2vpq7bfbzq