most citedEnd-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction

17 citations · 32 across the 2 of their papers we have counts for

collaborators

5 papers

cs.ET202017 cited

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…

cs.NE2020

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…

cs.ET201815 cited

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…

cs.ET2018

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…

cs.AI2018

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…