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Found 6 papers, 1 papers with code
Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training
However, the presence of a feedback loop from the readout to the recurrent layer de-stabilizes the learning mechanism and prevents it from converging.
A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks
We propose a design methodology to facilitate fault tolerance of deep learning models.
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
We propose a Deep Learning enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on infant's body.
Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review
Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design.
On the Mitigation of Read Disturbances in Neuromorphic Inference Hardware
Our analysis show both a strong dependency on model characteristics such as synaptic activation and criticality, and on the voltage used to read resistance states during inference.
Design Technology Co-Optimization for Neuromorphic Computing
We present a design-technology tradeoff analysis in implementing machine-learning inference on the processing cores of a Non-Volatile Memory (NVM)-based many-core neuromorphic hardware.
