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Found 6 papers, 1 papers with code
Magic for the Age of Quantized DNNs
Recently, the number of parameters in DNNs has explosively increased, as exemplified by LLMs (Large Language Models), making inference on small-scale computers more difficult.
Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks
Our proposed method can solve these problems; namely, SAF can halve the number of operations during the forward process, and it can be theoretically proven that SAF is consistent with the Spike Representation and OTTT, respectively.
Spiking Synaptic Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks
Spiking neural networks (SNNs) are energy-efficient neural networks because of their spiking nature.
Rethinking the role of normalization and residual blocks for spiking neural networks
Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption.
S$^3$NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks
To solve these problems, we propose a single-step spiking neural network (S$^3$NN), an energy-efficient neural network with low computational cost and high precision.
Spectral Pruning for Recurrent Neural Networks
Recurrent neural networks (RNNs) are a class of neural networks used in sequential tasks.
