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Found 7 papers, 0 papers with code
BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks
Particularly, model inversion (MI) attacks enable the reconstruction of data samples that have been used to train the model.
Neuromorphic Bayesian Optimization in Lava
The ever-increasing demands of computationally expensive and high-dimensional problems require novel optimization methods to find near-optimal solutions in a reasonable amount of time.
Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras
Specifically, the sensory information generated by event-based image sensors are orders of magnitude sparser compared to that of RGB sensors.
Biological connectomes as a representation for the architecture of artificial neural networks
Grand efforts in neuroscience are working toward mapping the connectomes of many new species, including the near completion of the Drosophila melanogaster.
IRIS: Integrated Retinal Functionality in Image Sensors
Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware.
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware.
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices.
