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Found 5 papers, 1 papers with code
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.
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.
Emerging Technologies
Spatio-temporal Learning with Arrays of Analog Nanosynapses
By translating the database into the time domain and using variable integration windows, up to 95% classification accuracy is achieved.
