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Found 5 papers, 0 papers with code
Bayesian Metaplasticity from Synaptic Uncertainty
Catastrophic forgetting remains a challenge for neural networks, especially in lifelong learning scenarios.
Synaptic metaplasticity with multi-level memristive devices
In this work, we extend this solution to quantized neural networks (QNNs) and present a memristor-based hardware solution for implementing metaplasticity during both inference and training.
Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks
However, the resistive change behavior in this regime suffers many fluctuations and is particularly challenging to model, especially in a way compatible with tools used for simulating deep learning.
In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications
With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling machine learning on the edge devices and avoiding data transfer over the network.
Spiking Neural Networks Hardware Implementations and Challenges: a Survey
Neuromorphic computing is henceforth a major research field for both academic and industrial actors.
