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Found 8 papers, 0 papers with code
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs
In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort.
Reinforcement Learning in a large scale photonic Recurrent Neural Network
Realizing photonic Neural Networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware was lacking so far.
Nonlinear memory capacity of parallel time-delay reservoir computers in the processing of multidimensional signals
This paper addresses the reservoir design problem in the context of delay-based reservoir computers for multidimensional input signals, parallel architectures, and real-time multitasking.
Fundamental aspects of noise in analog-hardware neural networks
The system under study consists of noisy linear nodes, and we investigate the signal-to-noise ratio at the network's outputs which is the upper limit to such a system's computing accuracy.
Reservoir-size dependent learning in analogue neural networks
However, important questions regarding impact of reservoir size and learning routines on the convergence-speed during learning remain unaddressed.
Three dimensional waveguide-interconnects for scalable integration of photonic neural networks
Photonic waveguides are prime candidates for integrated and parallel photonic interconnects.
Boolean learning under noise-perturbations in hardware neural networks
A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate.
Understanding and mitigating noise in trained deep neural networks
Here, we determine for the first time the propagation of noise in deep neural networks comprising noisy nonlinear neurons in trained fully connected layers.
