203 citations · 1.3k across the 57 of their papers we have counts for
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Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain
Zhuangyu Han, Abhronil Sengupta
Reinforcement learning (RL) has enabled robust quadruped locomotion over complex terrain, but most learned controllers are trained offline with backpropagation in massively paralle…
RMAAT: Astrocyte-Inspired Memory Compression and Replay for Efficient Long-Context Transformers
Md Zesun Ahmed Mia, Malyaban Bal, Abhronil Sengupta
The quadratic complexity of self-attention mechanism presents a significant impediment to applying Transformer models to long sequences. This work explores computational principles…
Spatio-Temporal Pruning for Compressed Spiking Large Language Models
Yi Jiang, Malyaban Bal, Brian Matejek +3
Large Language Models (LLMs) present significant challenges for deployment in energy-constrained environments due to their large model sizes and high inference latency. Spiking Neu…
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…
Astrocyte Regulated Neuromorphic Central Pattern Generator Control of Legged Robotic Locomotion
Zhuangyu Han, Abhronil Sengupta
Neuromorphic computing systems, where information is transmitted through action potentials in a bio-plausible fashion, is gaining increasing interest due to its promise of low-powe…
Delving Deeper Into Astromorphic Transformers
Md Zesun Ahmed Mia, Malyaban Bal, Abhronil Sengupta
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…
SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation
Malyaban Bal, Abhronil Sengupta
Large language Models (LLMs), though growing exceedingly powerful, comprises of orders of magnitude less neurons and synapses than the human brain. However, it requires significant…
Sequence Learning Using Equilibrium Propagation
Malyaban Bal, Abhronil Sengupta
Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative to conventional learning frameworks such as backpropagation. The effectiveness of EP stems from the fa…
Astromorphic Self-Repair of Neuromorphic Hardware Systems
Zhuangyu Han, A N M Nafiul Islam, Abhronil Sengupta
While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention i…
On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs
Mehul Rastogi, Sen Lu, Nafiul Islam +1
Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in…