29 citations · 72 across the 10 of their papers we have counts for
5 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…
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…
Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System
Abdullah M. Zyarah, Dhireesha Kudithipudi
Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems' ability to process…
Design Principles for Lifelong Learning AI Accelerators
Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah +9
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The…