29 citations · 72 across the 10 of their papers we have counts for
12 papers
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…
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…
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…
End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction
Abdullah M. Zyarah, Kevin Gomez, Dhireesha Kudithipudi
Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this paper, a neuromorphic system th…
Metaplasticity in Multistate Memristor Synaptic Networks
Fatima Tuz Zohora, Abdullah M. Zyarah, Nicholas Soures +1
Recent studies have shown that metaplastic synapses can retain information longer than simple binary synapses and are beneficial for continual learning. In this paper, we explore t…
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis
Abdullah M. Zyarah, Dhireesha Kudithipudi
Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatiotemporal inputs. This paper prese…
Semi-Trained Memristive Crossbar Computing Engine with In-Situ Learning Accelerator
Abdullah M. Zyarah, Dhireesha Kudithipudi
On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption. In this research, an on-device training circuitry for…
Neuromorphic Architecture for the Hierarchical Temporal Memory
Abdullah M. Zyarah, Dhireesha Kudithipudi
A biomimetic machine intelligence algorithm, that holds promise in creating invariant representations of spatiotemporal input streams is the hierarchical temporal memory (HTM). Thi…