79 citations · 135 across the 22 of their papers we have counts for
12 papers · 1 filter
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…