55 citations · 102 across the 17 of their papers we have counts for
7 papers · 1 filter
RobustNCL: Robustifying Neuromorphic Continual Learning under Transient Faults with Lightweight Detect-and-Cure Strategy
Rachmad Vidya Wicaksana Putra, Shuochen Zhou, Muhammad Shafique
In neural networks, continual learning (CL) is a prominent approach for enabling deployed systems to adapt to dynamic operational environments by effectively learning from new unse…
ENCL: Enabling Efficient Neuromorphic Continual Learning with Local Learning Rule and Sub-Network Freezing Strategy
Rachmad Vidya Wicaksana Putra, Shuochen Zhou, Hassan Raza +1
Continual learning (CL) in neural networks is a promising learning approach for updating the knowledge of deployed systems based on new data encountered at run time, that have not…
MTLiquid: Enabling Efficient Multi-Task Learning using Liquid Neural Networks for Lightweight Healthcare Monitoring Systems
Rachmad Vidya Wicaksana Putra, Fahad Abdul Rauf, Muhammad Shafique
Continuous-time sensing and monitoring with timely and accurate decision-making are critical for many real-world applications. In healthcare monitoring systems, physiological signa…
MorphAtt: A Neuromorphic Accelerator for Efficient Multi-Head Attention Processing in Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Amirhesam Jafari Rad, Muhammad Shafique
Spiking Vision Transformers (SViTs) are developed as an energy-efficient alternative to conventional ViTs for computer vision tasks at the edge. However, huge parameter counts and…
AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Saad Iftikhar, Muhammad Shafique
Spiking Vision Transformers (SViTs) have emerged as alternative low-power ViT models, but their large sizes hinder their deployments on resource-constrained embedded AI systems. To…
PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1
The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression. State-of-the-art works compress SViT…
QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models
Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique
Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (e.g., accuracy) and capabilities in ge…