Search Results for author:
Found 5 papers, 1 papers with code
Hierarchical Weight Averaging for Deep Neural Networks
Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs).
A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding
However, current accelerators for SNN cannot well support the emerging encoding schemes.
E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks with Emerging Neural Encoding on FPGAs
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators.
Efficient Spiking Neural Networks with Radix Encoding
Traditionally, a spike train needs around one thousand time steps to approach similar accuracy as its ANN counterpart.
Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data
As pointed in [17], ML models work well in the "training space" (i. e., feature space with sufficient training data), but they could not extrapolate beyond the training space.
