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Found 9 papers, 4 papers with code
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation.
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding.
Are SNNs Truly Energy-efficient? $-$ A Hardware Perspective
Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data representations.
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation.
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks
We propose Multiplier-less INTeger (MINT) quantization, a uniform quantization scheme that efficiently compresses weights and membrane potentials in spiking neural networks (SNNs).
Workload-Balanced Pruning for Sparse Spiking Neural Networks
Though the existing pruning methods can provide extremely high weight sparsity for deep SNNs, the high weight sparsity brings a workload imbalance problem.
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
SNNs allow spatio-temporal extraction of features and enjoy low-power computation with binary spikes.
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
To scale up a pruning technique towards deep SNNs, we investigate Lottery Ticket Hypothesis (LTH) which states that dense networks contain smaller subnetworks (i. e., winning tickets) that achieve comparable performance to the dense networks.
SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
Based on SATA, we show quantitative analyses of the energy efficiency of SNN training and compare the training cost of SNNs and ANNs.
