64 citations · 316 across the 28 of their papers we have counts for
6 papers · 1 filter
M2RU: Memristive Minion Recurrent Unit for On-Chip Continual Learning at the Edge
Abdullah M. Zyarah, Dhireesha Kudithipudi
Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical f…
Genesis: A Spiking Neuromorphic Accelerator With On-chip Continual Learning
Vedant Karia, Abdullah Zyarah, Dhireesha Kudithipudi
Continual learning, the ability to acquire and transfer knowledge through a models lifetime, is critical for artificial agents that interact in real-world environments. Biological…
Temporal Chunking Enhances Recognition of Implicit Sequential Patterns
Jayanta Dey, Nicholas Soures, Miranda Gonzales +3
In this pilot study, we propose a neuro-inspired approach that compresses temporal sequences into context-tagged chunks, where each tag represents a recurring structural unit or``c…
Voltage Mode Winner-Take-All Circuit for Neuromorphic Systems
Abdullah M. Zyarah, Dhireesha Kudithipudi
Recent advances in neuromorphic computing demonstrate on-device learning capabilities with low power consumption. One of the key learning units in these systems is the winner-take-…
Minion Gated Recurrent Unit for Continual Learning
Abdullah M. Zyarah, Dhireesha Kudithipudi
The increasing demand for continual learning in sequential data processing has led to progressively complex training methodologies and larger recurrent network architectures. Conse…
Reservoir Network with Structural Plasticity for Human Activity Recognition
Abdullah M. Zyarah, Alaa M. Abdul-Hadi, Dhireesha Kudithipudi
The unprecedented dissemination of edge devices is accompanied by a growing demand for neuromorphic chips that can process time-series data natively without cloud support. Echo sta…