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Found 8 papers, 3 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.
Synaptic metaplasticity in binarized neural networks
Unlike the brain, artificial neural networks, including state-of-the-art deep neural networks for computer vision, are subject to "catastrophic forgetting": they rapidly forget the previous task when trained on a new one.
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.
Synaptic Metaplasticity in Binarized Neural Networks
In this work, we interpret the hidden weights used by binarized neural networks, a low-precision version of deep neural networks, as metaplastic variables, and modify their training technique to alleviate forgetting.
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow improving the energy efficiency of Artificial Intelligence by orders of magnitude with regards to its implementation on computers and graphics cards.
Stochastic Computing for Hardware Implementation of Binarized Neural Networks
In this work, we propose a stochastic computing version of Binarized Neural Networks, where the input is also binarized.
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