8 papers
Scalable Equilibrium Propagation via Intermediate Error Signals for Deep Convolutional CRNNs
Jiaqi Lin, Malyaban Bal, Abhronil Sengupta
Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in which synaptic updates depend only o…
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…
GRASP: GRouped Activation Shared Parameterization for Parameter-Efficient Fine-Tuning and Robust Inference of Transformers
Malyaban Bal, Abhronil Sengupta
Parameter-efficient fine-tuning (PEFT) provides a scalable alternative to full-model adaptation by updating only a small subset of parameters in large pre-trained models. We introd…
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…
Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning
Md Zesun Ahmed Mia, Malyaban Bal, Sen Lu +4
Inspired by the brain's hierarchical processing and energy efficiency, this paper presents a Spiking Neural Network (SNN) architecture for lifelong Network Intrusion Detection Syst…
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…