203 citations · 1.3k across the 57 of their papers we have counts for
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Scaling SNNs Trained Using Equilibrium Propagation to Convolutional Architectures
Jiaqi Lin, Malyaban Bal, Abhronil Sengupta
Equilibrium Propagation (EP) is a biologically plausible local learning algorithm initially developed for convergent recurrent neural networks (RNNs), where weight updates rely sol…
Exploring Extreme Quantization in Spiking Language Models
Malyaban Bal, Yi Jiang, Abhronil Sengupta
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…
P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks
Malyaban Bal, Abhronil Sengupta
Spiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures, with their core computational…
Stochastic Spiking Neural Networks with First-to-Spike Coding
Yi Jiang, Sen Lu, Abhronil Sengupta
Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bio-plausibility and energy efficiency, especially when implemented on ne…
Benchmarking Spiking Neural Network Learning Methods with Varying Locality
Jiaqi Lin, Sen Lu, Malyaban Bal +1
Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have been shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machin…