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Found 6 papers, 2 papers with code
Scaling SNNs Trained Using Equilibrium Propagation to Convolutional Architectures
These results are comparable to those of convergent RNNs and SNNs trained by BPTT.
Exploring Extreme Quantization in Spiking Language Models
Despite the growing prevalence of large language model (LLM) architectures, a crucial concern persists regarding their energy and power consumption, which still lags far behind the remarkable energy efficiency of the human brain.
Benchmarking Spiking Neural Network Learning Methods with Varying Locality
However, training SNNs is challenging due to the non-differentiable nature of the spiking mechanism.
Delving Deeper Into Astromorphic Transformers
Preliminary attempts at incorporating the critical role of astrocytes - cells that constitute more than 50% of human brain cells - in brain-inspired neuromorphic computing remain in infancy.
SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation
Moreover, the convergence of average spiking rate of neurons at equilibrium is utilized to develop a novel ANN-SNN knowledge distillation based technique wherein we use a pre-trained BERT model as "teacher" to train our "student" spiking architecture.
Sequence Learning Using Equilibrium Propagation
However, by definition, EP requires the input to the model (a convergent RNN) to be static in both the phases of training.
