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Found 9 papers, 4 papers with code
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
CNN-based SNNs are the current mainstream of neuromorphic computing.
Inherent Redundancy in Spiking Neural Networks
In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs.
Spike-driven Transformer
In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike-driven Transformer with four unique properties: 1) Event-driven, no calculation is triggered when the input of Transformer is zero; 2) Binary spike communication, all matrix multiplications associated with the spike matrix can be transformed into sparse additions; 3) Self-attention with linear complexity at both token and channel dimensions; 4) The operations between spike-form Query, Key, and Value are mask and addition.
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network
LTH opens up a new path for network pruning.
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network
Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks.
Attention Spiking Neural Networks
On ImageNet-1K, we achieve top-1 accuracy of 75. 92% and 77. 08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs.
Advancing Spiking Neural Networks towards Deep Residual Learning
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice.
Temporal-wise Attention Spiking Neural Networks for Event Streams Classification
However, when aggregating individual events into frames with a new higher temporal resolution, existing SNN models do not attach importance to that the serial frames have different signal-to-noise ratios since event streams are sparse and non-uniform.
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Audio Classification
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Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs
Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition.
