Pinned Repositories
BNTT-Batch-Normalization-Through-Time
BNTT - Frontiers in Neuroscience (2021)
Exploring-Lottery-Ticket-Hypothesis-in-SNNs
Exploring Lottery Ticket Hypothesis in Sparse Spiking Neural Networks (ECCV2022, oral presentation)
Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks
PyTorch Implementation of Exploring Temporal Information Dynamics in Spiking Neural Networks (AAAI23)
FedSNN
Source code for the paper "Federated Learning with Spiking Neural Networks".
NDA_SNN
Pytorch implementation of Neuromorphic Data Augmentation for SNN, Accepted to ECCV 2022.
Neural-Architecture-Search-for-Spiking-Neural-Networks
Neural Architecture Search for Spiking Neural Networks, ECCV2022
Rate-vs-Direct
[ICASSP2022] RATE CODING OR DIRECT CODING: WHICH ONE IS BETTER FOR ACCURATE, ROBUST, and ENERGY-EFFICIENT SPIKING NEURAL NETWORKS
SATA
An energy simulation framework for BPTT-based SNN inference and training.
SNN_HAR
Pytorch implementation of Spiking Neural Networks for Human Activity Recognition.
SpikeSim
Intelligent Computing Lab at Yale University's Repositories
Intelligent-Computing-Lab-Yale/Neural-Architecture-Search-for-Spiking-Neural-Networks
Neural Architecture Search for Spiking Neural Networks, ECCV2022
Intelligent-Computing-Lab-Yale/BNTT-Batch-Normalization-Through-Time
BNTT - Frontiers in Neuroscience (2021)
Intelligent-Computing-Lab-Yale/Exploring-Lottery-Ticket-Hypothesis-in-SNNs
Exploring Lottery Ticket Hypothesis in Sparse Spiking Neural Networks (ECCV2022, oral presentation)
Intelligent-Computing-Lab-Yale/NDA_SNN
Pytorch implementation of Neuromorphic Data Augmentation for SNN, Accepted to ECCV 2022.
Intelligent-Computing-Lab-Yale/FedSNN
Source code for the paper "Federated Learning with Spiking Neural Networks".
Intelligent-Computing-Lab-Yale/SpikeSim
Intelligent-Computing-Lab-Yale/Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks
PyTorch Implementation of Exploring Temporal Information Dynamics in Spiking Neural Networks (AAAI23)
Intelligent-Computing-Lab-Yale/Rate-vs-Direct
[ICASSP2022] RATE CODING OR DIRECT CODING: WHICH ONE IS BETTER FOR ACCURATE, ROBUST, and ENERGY-EFFICIENT SPIKING NEURAL NETWORKS
Intelligent-Computing-Lab-Yale/SNN_HAR
Pytorch implementation of Spiking Neural Networks for Human Activity Recognition.
Intelligent-Computing-Lab-Yale/SATA
An energy simulation framework for BPTT-based SNN inference and training.
Intelligent-Computing-Lab-Yale/SNNCKA
Pytorch implementation of ANN-SNN representation similarity analysis (TMLR 2023)
Intelligent-Computing-Lab-Yale/SEENN
Pytorch implementation of SEENN (Spiking Early Exit Neural Networks) (NeurIPS 2023)
Intelligent-Computing-Lab-Yale/SNN-Segmentation
Intelligent-Computing-Lab-Yale/EfficientLIF-Net
Intelligent-Computing-Lab-Yale/PrivateSNN
[AAAI 2022] PrivateSNN: Fully Privacy-Preserving Spiking Neural Networks. https://arxiv.org/abs/2104.03414
Intelligent-Computing-Lab-Yale/Visual-Explanations-from-Spiking-Neural-Networks-using-Interspike-Intervals
Visual explanations from spiking neural networks using inter-spike intervals. Sci Rep 11, 19037 (2021).
Intelligent-Computing-Lab-Yale/Energy-Separation-Training
Intelligent-Computing-Lab-Yale/DetectX
Intelligent-Computing-Lab-Yale/SkipResConnection
Intelligent-Computing-Lab-Yale/XPert
Intelligent-Computing-Lab-Yale/MINT-Quantization
MINT, Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks, ASP-DAC 2024
Intelligent-Computing-Lab-Yale/PIVOT
Intelligent-Computing-Lab-Yale/u-Ticket-Pruning
workload balanced LTH-based pruning for SNNs