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Found 9 papers, 1 papers with code
SLSSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability.
Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator
Spiking neural network (SNN) is a brain-inspired model which has more spatio-temporal information processing capacity and computational energy efficiency.
Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block
We evaluate the proposed method for event streams classification tasks on neuromorphic N-MNIST, CIFAR10-DVS, DVS128 gesture datasets.
Self-Supervised Point Cloud Registration with Deep Versatile Descriptors
In this paper, we propose to jointly use both global and local descriptors to register point clouds in a self-supervised manner, which is motivated by a critical observation that all local geometries of point clouds are transformed consistently under the same transformation.
Direct Training via Backpropagation for Ultra-low Latency Spiking Neural Networks with Multi-threshold
In our proposed training method, we proposed three approximated derivative for spike activity to solve the problem of the non-differentiable issue which cause difficulties for direct training SNNs based on BP.
Machine Learning Regression based Single Event Transient Modeling Method for Circuit-Level Simulation
A multilayer feedfordward neural network is used to build the SET pulse current model by learning the data from TCAD simulation.
Multi-dimensional wave steering with higher-order topological phononic crystal
The recent discovery and realizations of higher-order topological insulators enrich the fundamental studies on topological phases.
Applied Physics
A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation
We prove that CAT is a rotation and translation-invariant transformation based on the theoretical analysis.
Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes
Using spiking speech and image recognition datasets, we demonstrate the feasibility of supporting large time compression ratios of up to 16x, delivering up to 15. 93x, 13. 88x, and 86. 21x improvements in throughput, energy dissipation, the tradeoffs between hardware area, runtime, energy, and classification accuracy, respectively based on different spike codes on a Xilinx Zynq-7000 FPGA.
