9 papers
SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition
Jayanta Dey, Shikhar Srivastava, Itamar Lerner +2
Learning long-range non-stationary temporal patterns remains a core challenge for modern sequence models, particularly in strict streaming settings. In these settings, data arrive…
Position: Modular Memory is the Key to Continual Learning Agents
Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov +21
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these…
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-…